[FreeCoursesOnline.Me] U-M.L.E-N v1.0.0

磁力链接/BT种子名称

[FreeCoursesOnline.Me] U-M.L.E-N v1.0.0

磁力链接/BT种子简介

种子哈希：c73ebc5ebf895b71347f54de8387b8f4d7cf6501
文件大小： 5.36G
已经下载：1236次
下载速度：极快
收录时间：2021-03-12
最近下载：2024-11-17

移花宫入口

移花宫.com邀月.com怜星.com花无缺.comyhgbt.icuyhgbt.top

磁力链接下载

magnet:?xt=urn:btih:C73EBC5EBF895B71347F54DE8387B8F4D7CF6501
推荐使用PIKPAK网盘下载资源，10TB超大空间，不限制资源，无限次数离线下载，视频在线观看

下载BT种子文件

磁力链接 迅雷下载 PIKPAK在线播放 91视频 含羞草 欲漫涩 逼哩逼哩 成人快手 51品茶 抖阴破解版 暗网禁地 91短视频 TikTok成人版 PornHub 草榴社区 乱伦社区

最近搜索

文件列表

  • Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.mp4 110.1 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.mp4 109.7 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.mp4 77.9 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.mp4 72.1 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.mp4 66.7 MB
  • Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4 52.2 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.mp4 51.2 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.mp4 50.7 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.mp4 46.3 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.mp4 45.7 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4 42.7 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4 42.7 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.mp4 41.3 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.mp4 35.0 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.mp4 34.8 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.mp4 34.3 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.mp4 34.2 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4 34.1 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.mp4 34.1 MB
  • Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.mp4 34.0 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.mp4 33.2 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4 33.2 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.mp4 32.6 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.mp4 31.9 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.mp4 31.6 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.mp4 30.1 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.mp4 28.9 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4 28.1 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4 27.9 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4 27.6 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.mp4 26.9 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4 26.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4 25.9 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4 24.5 MB
  • Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4 24.4 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.mp4 24.3 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4 23.6 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.mp4 23.1 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.mp4 23.1 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4 22.8 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4 22.7 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4 22.4 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4 22.1 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.mp4 22.0 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.mp4 22.0 MB
  • Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.mp4 21.9 MB
  • Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.mp4 21.8 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4 21.7 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.mp4 21.7 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4 21.6 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.mp4 21.2 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.mp4 21.1 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.mp4 21.0 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4 20.9 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4 20.9 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.mp4 20.7 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.mp4 19.8 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4 19.7 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4 19.5 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.mp4 19.3 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.mp4 19.3 MB
  • Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.mp4 19.3 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.mp4 19.1 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4 19.0 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4 19.0 MB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.mp4 19.0 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.mp4 18.6 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4 18.6 MB
  • Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.mp4 18.4 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4 18.3 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.mp4 18.2 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.mp4 18.2 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.mp4 18.0 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4 17.9 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.mp4 17.9 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.mp4 17.8 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.mp4 17.7 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.mp4 17.5 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4 17.5 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.mp4 17.5 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.mp4 17.4 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.mp4 17.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4 17.3 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.mp4 17.2 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.mp4 16.9 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.mp4 16.4 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4 16.2 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.mp4 16.0 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.mp4 15.9 MB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.mp4 15.7 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.mp4 15.6 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.mp4 15.6 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.mp4 15.5 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.mp4 15.4 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.mp4 15.3 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.mp4 15.1 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.mp4 15.1 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4 15.1 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.mp4 15.0 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.mp4 15.0 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.mp4 14.8 MB
  • Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.mp4 14.7 MB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.mp4 14.5 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.mp4 14.4 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.mp4 14.4 MB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.mp4 14.1 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4 14.0 MB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.mp4 13.9 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4 13.9 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.mp4 13.8 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4 13.8 MB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.mp4 13.8 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4 13.8 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4 13.6 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4 13.6 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.mp4 13.5 MB
  • Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4 13.3 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4 13.3 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.mp4 13.3 MB
  • Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.mp4 13.2 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.mp4 13.2 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4 13.2 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.mp4 13.2 MB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.mp4 13.1 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.mp4 13.1 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4 13.1 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4 12.9 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.mp4 12.8 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.mp4 12.8 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.mp4 12.7 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.mp4 12.7 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4 12.6 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.mp4 12.5 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.mp4 12.4 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4 12.3 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4 12.1 MB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.mp4 12.1 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.mp4 12.1 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.mp4 11.8 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.mp4 11.6 MB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.mp4 11.3 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4 11.2 MB
  • Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4 11.0 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.mp4 11.0 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.mp4 10.9 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4 10.9 MB
  • Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.mp4 10.8 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.mp4 10.8 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4 10.8 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.mp4 10.8 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.mp4 10.7 MB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.mp4 10.6 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4 10.6 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4 10.5 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.mp4 10.5 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.mp4 10.4 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.mp4 10.4 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4 10.4 MB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.mp4 10.3 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.mp4 10.3 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.mp4 10.2 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.mp4 10.2 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.mp4 10.2 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4 10.2 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.mp4 10.1 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4 9.9 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.mp4 9.9 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.mp4 9.9 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.mp4 9.9 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4 9.8 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4 9.7 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.mp4 9.7 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.mp4 9.7 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.mp4 9.7 MB
  • Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4 9.7 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4 9.6 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4 9.6 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4 9.6 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.mp4 9.6 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.mp4 9.6 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.mp4 9.5 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4 9.5 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.mp4 9.4 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.mp4 9.3 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.mp4 9.3 MB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.mp4 9.3 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4 9.2 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4 9.1 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4 9.1 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4 8.9 MB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.mp4 8.9 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.mp4 8.9 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.mp4 8.9 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.mp4 8.8 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.mp4 8.8 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4 8.8 MB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.mp4 8.7 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.mp4 8.6 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.mp4 8.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.mp4 8.6 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.mp4 8.6 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.mp4 8.5 MB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.mp4 8.5 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 8.5 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4 8.4 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4 8.4 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4 8.4 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4 8.4 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4 8.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.mp4 8.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.mp4 8.3 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.mp4 8.2 MB
  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.mp4 8.2 MB
  • Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.mp4 8.1 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.mp4 8.1 MB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.mp4 8.1 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.mp4 8.0 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.mp4 8.0 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4 8.0 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.mp4 8.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4 7.9 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/chess-game.jpg 7.9 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4 7.9 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4 7.8 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.mp4 7.7 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.mp4 7.7 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.mp4 7.7 MB
  • Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4 7.7 MB
  • Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.mp4 7.6 MB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4 7.6 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4 7.6 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4 7.6 MB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.mp4 7.6 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.mp4 7.5 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4 7.4 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4 7.4 MB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.mp4 7.4 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.mp4 7.3 MB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.mp4 7.3 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.mp4 7.3 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.mp4 7.3 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 7.3 MB
  • Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4 7.3 MB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.mp4 7.2 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4 7.2 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.mp4 7.2 MB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.mp4 7.2 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.mp4 7.1 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.mp4 7.1 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.mp4 7.1 MB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.mp4 7.0 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.mp4 7.0 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.mp4 7.0 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.mp4 7.0 MB
  • Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.mp4 7.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4 6.9 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.mp4 6.9 MB
  • Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4 6.9 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4 6.8 MB
  • Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.mp4 6.8 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.mp4 6.8 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm.mp4 6.7 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.mp4 6.7 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4 6.6 MB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.mp4 6.6 MB
  • Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.mp4 6.6 MB
  • Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.mp4 6.6 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.mp4 6.5 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4 6.5 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.mp4 6.4 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4 6.4 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.mp4 6.4 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4 6.4 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.mp4 6.3 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.mp4 6.3 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4 6.3 MB
  • Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4 6.3 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.mp4 6.3 MB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.mp4 6.3 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.mp4 6.3 MB
  • Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.mp4 6.3 MB
  • Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.mp4 6.3 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4 6.2 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4 6.1 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4 6.1 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.mp4 6.1 MB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.mp4 6.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4 6.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 6.0 MB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.mp4 5.9 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.mp4 5.9 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.mp4 5.9 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4 5.8 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.mp4 5.8 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.mp4 5.8 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.mp4 5.7 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4 5.7 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4 5.7 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4 5.7 MB
  • Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4 5.7 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.mp4 5.7 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4 5.6 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 5.6 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 5.6 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.mp4 5.5 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4 5.5 MB
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.mp4 5.4 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4 5.4 MB
  • Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.mp4 5.4 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4 5.4 MB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4 5.4 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 5.4 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4 5.3 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.3 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4 5.3 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.mp4 5.3 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.mp4 5.2 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.mp4 5.2 MB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.mp4 5.2 MB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.mp4 5.2 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.mp4 5.1 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.mp4 5.1 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.mp4 5.1 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.mp4 5.1 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4 5.0 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.mp4 5.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.mp4 5.0 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.mp4 5.0 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.mp4 4.9 MB
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.mp4 4.9 MB
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4 4.9 MB
  • Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.mp4 4.8 MB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4 4.8 MB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.mp4 4.8 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.mp4 4.7 MB
  • Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.mp4 4.7 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.mp4 4.6 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.mp4 4.6 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4 4.5 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4 4.5 MB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.mp4 4.5 MB
  • Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.mp4 4.5 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.mp4 4.5 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4 4.4 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.mp4 4.4 MB
  • Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.mp4 4.4 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.mp4 4.4 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4 4.4 MB
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.mp4 4.4 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4 4.4 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 4.3 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.mp4 4.3 MB
  • Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.mp4 4.3 MB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.mp4 4.3 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4 4.3 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.mp4 4.3 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.mp4 4.2 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 4.2 MB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.mp4 4.2 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.mp4 4.2 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.mp4 4.2 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 4.1 MB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.mp4 4.1 MB
  • Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.mp4 4.1 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.mp4 4.1 MB
  • Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.mp4 4.1 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4 4.1 MB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.mp4 4.1 MB
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.mp4 4.1 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.mp4 4.0 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.mp4 4.0 MB
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.mp4 4.0 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4 4.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 4.0 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.mp4 4.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4 3.9 MB
  • Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.mp4 3.9 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.mp4 3.9 MB
  • Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.mp4 3.9 MB
  • Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.mp4 3.9 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.8 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 3.8 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.mp4 3.8 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4 3.8 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4 3.8 MB
  • Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.mp4 3.8 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4 3.7 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.mp4 3.7 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.mp4 3.7 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png 3.7 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png 3.7 MB
  • Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.mp4 3.7 MB
  • Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.mp4 3.6 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.mp4 3.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.mp4 3.6 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.mp4 3.6 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.mp4 3.6 MB
  • Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.mp4 3.6 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.mp4 3.6 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.mp4 3.6 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4 3.5 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.mp4 3.5 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.mp4 3.5 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.mp4 3.5 MB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.mp4 3.5 MB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.mp4 3.5 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 3.5 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/29. Neural Networks Outro V2-pwA5shUkRVc.mp4 3.5 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.mp4 3.5 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4 3.4 MB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.mp4 3.4 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.mp4 3.4 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.mp4 3.4 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4 3.4 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 3.4 MB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.mp4 3.3 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.mp4 3.3 MB
  • Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.mp4 3.3 MB
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.mp4 3.3 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.mp4 3.3 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4 3.2 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.mp4 3.2 MB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4 3.2 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.2 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png 3.2 MB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4 3.2 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.mp4 3.2 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.mp4 3.2 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4 3.2 MB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.mp4 3.2 MB
  • Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.mp4 3.1 MB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4 3.1 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.mp4 3.1 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.mp4 3.1 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4 3.1 MB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.mp4 3.0 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 3.0 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png 3.0 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 3.0 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4 3.0 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.mp4 3.0 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.mp4 3.0 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.mp4 3.0 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.mp4 3.0 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.mp4 3.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 3.0 MB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.mp4 3.0 MB
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.mp4 3.0 MB
  • Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.mp4 2.9 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.mp4 2.9 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.mp4 2.9 MB
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.mp4 2.9 MB
  • Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.mp4 2.9 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.mp4 2.9 MB
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.mp4 2.9 MB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.mp4 2.9 MB
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.mp4 2.9 MB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-agent-monitor-main.gif 2.9 MB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.mp4 2.9 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.mp4 2.9 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.mp4 2.8 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.mp4 2.8 MB
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.mp4 2.8 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4 2.8 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.mp4 2.8 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4 2.8 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.mp4 2.7 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4 2.7 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4 2.7 MB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.mp4 2.7 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.mp4 2.7 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.mp4 2.7 MB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.mp4 2.7 MB
  • Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.mp4 2.6 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.mp4 2.6 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4 2.6 MB
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.mp4 2.6 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.mp4 2.6 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.mp4 2.6 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.mp4 2.6 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.mp4 2.6 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.mp4 2.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.mp4 2.5 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4 2.5 MB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.mp4 2.5 MB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.mp4 2.5 MB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.mp4 2.5 MB
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.mp4 2.5 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.mp4 2.5 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4 2.5 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.mp4 2.4 MB
  • Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.mp4 2.4 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.mp4 2.4 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4 2.4 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.mp4 2.4 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.mp4 2.4 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.mp4 2.4 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4 2.4 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.mp4 2.4 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.mp4 2.4 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4 2.3 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.mp4 2.3 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4 2.3 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.mp4 2.3 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.mp4 2.3 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.mp4 2.3 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4 2.3 MB
  • Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4 2.3 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.mp4 2.3 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.mp4 2.3 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.mp4 2.2 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.mp4 2.2 MB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.mp4 2.2 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4 2.2 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.mp4 2.2 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.mp4 2.2 MB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.mp4 2.2 MB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.mp4 2.2 MB
  • Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.mp4 2.1 MB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.mp4 2.1 MB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/run-main.gif 2.1 MB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.mp4 2.1 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4 2.1 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4 2.0 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.mp4 2.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4 2.0 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.mp4 2.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4 2.0 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.mp4 1.9 MB
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.mp4 1.9 MB
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.mp4 1.9 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4 1.9 MB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.mp4 1.9 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.mp4 1.8 MB
  • Part 02-Module 03-Lesson 01_Model Selection/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4 1.8 MB
  • Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.mp4 1.8 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.mp4 1.8 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4 1.8 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 1.8 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.mp4 1.8 MB
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.mp4 1.8 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4 1.7 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/skin-disease-classes.png 1.7 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4 1.7 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.mp4 1.7 MB
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.mp4 1.7 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.mp4 1.7 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.mp4 1.7 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4 1.7 MB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.mp4 1.7 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.mp4 1.7 MB
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.mp4 1.7 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.mp4 1.7 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4 1.7 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.mp4 1.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/lesions.png 1.6 MB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.mp4 1.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.mp4 1.6 MB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4 1.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.mp4 1.6 MB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.mp4 1.6 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/frozen-lake-6.jpg 1.6 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 1.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.mp4 1.6 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4 1.6 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.mp4 1.5 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.mp4 1.5 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.mp4 1.5 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.mp4 1.5 MB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4 1.5 MB
  • Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.mp4 1.5 MB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.mp4 1.5 MB
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.mp4 1.4 MB
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.mp4 1.4 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4 1.4 MB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.mp4 1.4 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.mp4 1.4 MB
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.mp4 1.4 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.mp4 1.3 MB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.mp4 1.3 MB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw.mp4 1.3 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.mp4 1.3 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 09-Module 02-Lesson 01_GitHub Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/img/screen-shot-2017-10-31-at-1.06.42-pm.png 1.3 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/arch.png 1.3 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.mp4 1.2 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/convolutionalnetworksquiz.png 1.2 MB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.mp4 1.2 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.mp4 1.2 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.mp4 1.2 MB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.mp4 1.2 MB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.mp4 1.2 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.mp4 1.2 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.mp4 1.2 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.mp4 1.2 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.mp4 1.2 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.mp4 1.2 MB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.mp4 1.2 MB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.mp4 1.2 MB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.mp4 1.1 MB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.mp4 1.1 MB
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.mp4 1.1 MB
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.mp4 1.1 MB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.mp4 1.1 MB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.mp4 1.1 MB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.mp4 1.1 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.mp4 1.0 MB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/statevalue.png 1.0 MB
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.mp4 1.0 MB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/logistic-regression-quiz.png 1.0 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.mp4 1.0 MB
  • Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.mp4 1.0 MB
  • Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.mp4 1.0 MB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.mp4 969.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.mp4 969.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.mp4 949.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/nature.png 914.5 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.mp4 909.9 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.mp4 894.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.mp4 883.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.mp4 874.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.mp4 839.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.mp4 839.5 kB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-terminal.gif 838.9 kB
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.mp4 823.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.mp4 800.8 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/sebastian-katie-jay.png 798.5 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/img/get-hired-with-the-udacity-career-portal.gif 774.9 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/student-quiz.png 767.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/student-quiz.png 767.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.mp4 763.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.mp4 750.0 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-4.58.58-pm.png 733.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/img/6509638772.gif 728.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.mp4 725.9 kB
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.mp4 719.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.mp4 709.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.mp4 676.1 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/img/screen-shot-2018-01-03-at-2.20.30-pm.png 662.9 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.mp4 647.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/actionvalue.png 643.5 kB
  • Part 02-Module 03-Lesson 01_Model Selection/img/models.png 643.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-24-at-4.28.04-pm.png 637.6 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/go.jpg 629.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/and-to-or.png 620.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/and-to-or.png 620.7 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.mp4 612.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.mp4 609.8 kB
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.mp4 591.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/cat-3.png 589.7 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.mp4 587.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.mp4 583.0 kB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.mp4 570.0 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/img/submit-workspace.png 559.8 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.mp4 559.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/3219238538.gif 524.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/3204138549.gif 508.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/house.png 503.3 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.mp4 495.5 kB
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.mp4 484.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/threshold.png 479.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/3214548558.gif 479.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/3204388552.gif 474.8 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-08-31-at-3.27.10-pm.png 474.2 kB
  • assets/img/udacimak.png 472.1 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/3215618544.gif 471.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/img/6485174133.gif 469.1 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/img/6499079068.gif 456.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/img/6551597473.gif 455.0 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/retriever-patch-shifted.png 453.9 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-11-26-at-9.38.24-am.png 451.5 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2991788616.gif 449.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/retriever-patch.png 446.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.mp4 432.7 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/img/regularization-quiz.png 431.0 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-11-26-at-9.55.20-am.png 424.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-09-26-at-2.18.38-pm.png 415.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3013998667.gif 414.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. Images-1GdiN5Wc8LA.mp4 404.9 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.mp4 404.5 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/or-quiz.png 403.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/or-quiz.png 403.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/value-iteration.png 390.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.008.jpeg 378.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-pred-action.png 372.3 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2944258660.gif 363.4 kB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.mp4 359.1 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2963418671.gif 356.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-pred-state.png 356.5 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3075798615.gif 350.3 kB
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.mp4 349.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/vlcsnap-2016-11-24-16h01m35s262.png 349.5 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2970968572.gif 345.2 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/img/fbeta.png 345.2 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2985858609.gif 344.6 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/screen-shot-2017-12-17-at-12.49.34-pm.png 340.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/teeth-whiskers-tongue.png 339.9 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/2949288751.gif 336.9 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3079068542.gif 335.5 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/img/7905614952.gif 333.3 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2966288580.gif 326.5 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2946478670.gif 322.6 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-career-service-example.png 322.5 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/td-prediction.png 318.6 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/confusion-matrix.png 318.4 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/img/atari-network.png 317.4 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2962878580.gif 316.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/img/all-ranks.png 315.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/a-b-c-fill-nn.png 312.8 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/img/step-0.png 309.2 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/img/step-0.png 309.2 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/img/step-0.png 309.2 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/trees.png 307.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-control-glie.png 304.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/img/7910014174.gif 304.2 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/img/7881207114.gif 298.3 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/img/step1-file-upload.png 297.7 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/img/step1-file-upload.png 297.7 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/img/step1-file-upload.png 297.7 kB
  • Part 10-Module 02-Lesson 06_Graphs/img/7919804788.gif 295.6 kB
  • Part 10-Module 02-Lesson 06_Graphs/media/unnamed-69567-0.gif 295.6 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3094188555.gif 294.2 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/sarsa.png 293.7 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/img/layers.png 293.0 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/screen-shot-2018-01-06-at-10.44.48-pm.png 292.3 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.mp4 289.2 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.005.jpeg 288.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/vlcsnap-2016-11-24-15h52m47s438.png 287.0 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2959748717.gif 282.8 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2018-01-08-at-5.38.03-am.png 282.8 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/mc-control-constant-a.png 281.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/truncated-iter.png 280.6 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.004.jpeg 279.4 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-5.01.26-pm.png 278.4 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/and-quiz.png 272.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/and-quiz.png 272.2 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/sarsamax.png 270.9 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3090048570.gif 269.3 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3099598537.gif 269.1 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3097488603.gif 268.1 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4 266.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4 266.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/policy-eval.png 265.9 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-09-26-at-11.03.16-pm.png 265.9 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3073008570.gif 265.4 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-06.png 265.3 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/img/step-2-file-upload.png 264.5 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/img/step-2-file-upload.png 264.5 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/img/step-2-file-upload.png 264.5 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/screen-shot-2018-06-12-at-5.07.10-pm.png 263.6 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/2967238555.gif 263.1 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-04.png 261.3 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3059748569.gif 261.0 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/expected-sarsa.png 260.5 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3095478574.gif 260.0 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.003.jpeg 259.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-01.png 257.3 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/img/precision-quiz.png 256.8 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/matengai-of-kuniga-coast-in-oki-island-shimane-pref600.jpg 252.9 kB
  • Part 10-Module 02-Lesson 05_Trees/img/tree-traversal-practice.jpg 252.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-10.png 247.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-08.png 247.4 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/iteration.png 247.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/2981618588.gif 240.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-07.png 238.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/perceptron-graphics.001.jpeg 238.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-05.png 238.1 kB
  • assets/js/katex.min.js 236.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/cat-2.jpeg 236.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/cat-1.jpeg 236.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/2956218691.gif 235.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-03.png 234.4 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/img/recall-quiz.png 233.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-09.png 233.5 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3065198593.gif 233.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.001.jpeg 231.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/truncated-eval.png 230.6 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-plot-perceptron-combine.png 230.3 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/dog-1210559-1280.jpg 228.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/full-padding-no-strides-transposed.gif 227.1 kB
  • index.html 226.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-09-26-at-4.22.09-pm.png 224.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/02-guide-how-transfer-learning-v3-02.png 224.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/margin-geometry-images.002.jpeg 220.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/xor.png 220.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/xor.png 220.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/img/multi-layer.png 219.5 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/img/7889679710.gif 218.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-12.20.50-pm.png 215.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/meme.png 214.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/meme.png 214.1 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/img/meme.png 214.1 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/meme.png 214.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/meme.png 214.1 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/exploration-vs.-exploitation.png 209.2 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-12.20.30-pm.png 208.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-21-at-12.20.30-pm.png 208.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3081768538.gif 207.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-plot-perceptron-combine-v2.png 205.7 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/img/7890272657.gif 202.3 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/batch-stochastic.png 201.6 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-06-13-at-12.58.03-pm.png 201.0 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3083018581.gif 199.8 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3050028596.gif 196.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/table.png 196.7 kB
  • Part 10-Module 02-Lesson 05_Trees/img/7900766165.gif 195.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/media/monkey-doctor.png 194.5 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/img/7883232307.gif 194.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.mp4 193.9 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.mp4 193.4 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/img/confusion.png 193.4 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/media/unnamed-5101-0.gif 193.3 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/img/curves.png 193.0 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/p2-limit-increase.png 192.7 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/img/screen-shot-2018-01-03-at-2.23.38-pm.png 192.4 kB
  • Part 04-Module 04-Lesson 01_PCA/img/2979238559.gif 191.5 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/img/medical.png 191.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/new-confusion-matrix.png 190.6 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/1omsg2-mkguagky1c64uflw.gif 188.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3056738546.gif 188.1 kB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/new-tab.gif 185.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/pup.jpg 185.6 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/mat-headshot.png 184.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/2-card-21.png 180.1 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/quiz.jpg 178.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3034378634.gif 177.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/img/eeg-ica.png 175.0 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/img/naive-bayes-quiz.png 170.4 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.49.43-pm.png 169.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3004978616.gif 168.5 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3059228570.gif 163.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-12-17-at-9.41.03-am.png 162.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/precision-recall.png 160.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/img/3076888537.gif 160.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/sensitivity-specificity.png 158.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-3.08.03-pm.png 156.6 kB
  • Part 04-Module 04-Lesson 01_PCA/img/3062928590.gif 156.5 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/incremental.png 155.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/est-action.png 154.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3040398570.gif 152.3 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/img/email.png 152.1 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/img/parrot-ar-drone.jpg 150.0 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2017-11-26-at-10.30.15-am.png 148.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/constant-alpha.png 147.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/recommending-apps.png 143.9 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-body-good.png 143.4 kB
  • assets/css/bootstrap.min.css 140.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/roc-curves.png 140.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/minibatch.png 140.0 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/img/spamham.png 138.3 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/screen-shot-2018-08-17-at-2.07.46-pm.png 137.3 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2018-07-19-at-5.39.37-pm.png 134.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/sample-confusion-matrix.png 133.7 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/p2xlarge-limit-request.png 132.8 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/screen-shot-2017-08-09-at-7.09.54-pm.png 132.0 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/img/poker-hand-3-of-a-kind.png 131.7 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/filter-depth.png 130.8 kB
  • assets/js/plyr.polyfilled.min.js 129.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/img/3058428551.gif 127.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/improve.png 127.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/admissions-data.png 121.2 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/img/decision-trees.png 119.8 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-perceptron.png 118.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/backgammonboard.svg.png 115.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-01-06-at-9.41.01-pm.png 113.4 kB
  • Part 02-Module 03-Lesson 01_Model Selection/img/learning-curves.png 111.6 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/amazonwebservices-logo.svg.png 109.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/img/nn.png 108.5 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/img/accuracy-quiz.png 108.4 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/article-2278590-1792e332000005dc-394-634x615.jpg 105.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/legend.png 104.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/img/kernel-trick.png 101.2 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/external-indices-quiz.png 98.8 kB
  • Part 02-Module 03-Lesson 01_Model Selection/img/complexity.png 97.9 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/media/unnamed-project-desc-0.gif 96.8 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/xor-quiz.png 96.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/xor-quiz.png 96.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/img/summary.png 96.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/perceptronquiz.png 95.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/perceptronquiz.png 95.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-and-or-percep-fixed.png 94.8 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/example-data.png 94.3 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/student-data.png 94.1 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/screen-shot-2018-08-17-at-2.07.36-pm.png 93.4 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/162524.gif 90.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/img/regularization-quiz.png 90.0 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/tensorflow.png 87.3 kB
  • assets/js/jquery-3.3.1.min.js 86.9 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-05-at-3.55.40-pm.png 86.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/gmm-quiz.png 82.6 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/polynomial-kernel-2-quiz.png 81.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/matrix-mult-3.png 80.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/roc.png 80.9 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-6.02.37-pm.png 80.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/gmm-2d-quiz.png 80.3 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/img/linear-boundary.png 77.0 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-12-at-5.47.45-pm.png 75.4 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/img/enable-gpu.png 75.2 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/gradient-descent.png 73.7 kB
  • assets/css/fonts/KaTeX_AMS-Regular.ttf 71.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/and-table.png 70.8 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/just-a-2d-reg.png 70.1 kB
  • assets/css/fonts/KaTeX_Main-Regular.ttf 70.1 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/img/spam.png 69.4 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/img/paper-notes.svg.png 69.0 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-01-06-at-9.30.27-pm.png 68.0 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-intro-good.png 67.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/example-after-bias.png 67.3 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.50.54-pm.png 66.2 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-5.51.40-pm.png 66.1 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/convolution-schematic.gif 65.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/convolution-schematic.gif 65.2 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/img/points.png 64.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/points.png 64.7 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/img/dropout-node.jpeg 64.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/cross-entropy-diagram.png 64.2 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-good-conclusion.png 63.8 kB
  • assets/css/fonts/KaTeX_Main-Bold.ttf 61.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/network-with-labeled-weights.png 60.9 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-2.46.11-pm.png 60.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/sigmoids.png 59.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.49.08-pm.png 58.7 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/img/screen-shot-2017-10-17-at-11.02.44-am.png 57.9 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/amazon-aws.png 57.3 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-3.25.10-pm.png 56.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/derivative-example.png 56.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/notmnist.png 55.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/heirarchy-diagram.jpg 54.9 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/img/points.png 54.7 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-9.18.00-pm.png 53.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/softmax-input-output.png 53.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-3.46.12-pm.png 53.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/network-with-labeled-nodes.png 53.2 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/input-times-weights.png 53.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-01-06-at-8.13.20-pm.png 52.0 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/img/screen-shot-2018-01-06-at-8.13.20-pm.png 52.0 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/img/circle-data.png 51.1 kB
  • Part 02-Module 03-Lesson 01_Model Selection/img/circle-data.png 51.1 kB
  • assets/js/bootstrap.min.js 51.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/img/data.png 50.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/simple-neuron.png 50.3 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/multilayer-diagram-weights.png 49.7 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-12-14-at-3.11.32-pm.png 49.1 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/stop.png 48.7 kB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/screen-shot-2018-04-14-at-3.13.15-pm.png 48.2 kB
  • assets/css/fonts/KaTeX_Main-Italic.ttf 48.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/sample-roc-curve.png 47.4 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/layer-1-grid.png 46.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/layer-1-grid.png 46.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/img/svm-image.png 46.2 kB
  • assets/js/jquery.mCustomScrollbar.concat.min.js 45.5 kB
  • assets/css/fonts/KaTeX_Main-BoldItalic.ttf 44.8 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/cover-letter-intro-bad.png 43.3 kB
  • assets/css/jquery.mCustomScrollbar.min.css 42.8 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/img/eggsdata.png 42.8 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/aws-add-sec-group.png 42.7 kB
  • assets/css/fonts/KaTeX_Math-Italic.ttf 41.4 kB
  • assets/css/fonts/KaTeX_AMS-Regular.woff 40.2 kB
  • assets/css/fonts/KaTeX_Math-BoldItalic.ttf 39.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hq-new-xor-table.png 39.5 kB
  • assets/css/fonts/KaTeX_Main-Regular.woff 39.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/local-minima.png 39.0 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/img/udacitylogo-copy.png 38.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/maxpool.jpeg 38.0 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/maxpool.jpeg 38.0 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/example-before-bias.png 37.8 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/img/semi-supervised-learning.jpg 37.7 kB
  • assets/css/fonts/KaTeX_Main-Bold.woff 36.8 kB
  • assets/css/fonts/KaTeX_Typewriter-Regular.ttf 36.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/grid-layer-1.png 36.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/grid-layer-1.png 36.1 kB
  • assets/css/fonts/KaTeX_Fraktur-Bold.ttf 36.0 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-09-04-at-2.07.44-pm.png 34.9 kB
  • assets/css/fonts/KaTeX_Fraktur-Regular.ttf 34.7 kB
  • assets/css/fonts/KaTeX_SansSerif-Bold.ttf 34.0 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/screen-shot-2018-01-08-at-5.37.22-am.png 34.0 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/relu.png 33.9 kB
  • Part 04-Module 04-Lesson 01_PCA/media/unnamed-134180-instructor-note-0.gif 33.6 kB
  • assets/css/fonts/KaTeX_AMS-Regular.woff2 33.2 kB
  • assets/css/fonts/KaTeX_Main-Regular.woff2 32.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/roc-curve.png 32.2 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/img/relu-network.png 31.8 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/session.png 31.6 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/img/screen-shot-2018-07-27-at-1.24.38-pm.png 31.6 kB
  • assets/css/fonts/KaTeX_SansSerif-Italic.ttf 31.3 kB
  • assets/css/fonts/KaTeX_Main-Bold.woff2 30.6 kB
  • assets/css/fonts/KaTeX_SansSerif-Regular.ttf 30.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/pooling-dims.png 29.9 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/media/unnamed-135397-0.gif 29.5 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/lin-reg-no-outliers.png 29.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/conv-dims.png 29.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/8733666934.gif 28.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/8733666938.gif 28.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/8733666942.gif 28.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/8733666946.gif 28.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/8733666950.gif 28.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/8733666954.gif 28.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/sigmoid.png 28.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/06-l-supervised-classification-391-1.jpg 28.3 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-20-at-12.02.06-pm.png 28.3 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/lin-reg-w-outliers.png 28.2 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/13. Implementing Gradient Descent.html 28.1 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/softmax.png 27.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/24. Quiz Mini-batch.html 27.6 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/screen-shot-2017-09-21-at-4.34.08-pm.png 27.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/img/gmm-1d-quiz.png 27.4 kB
  • assets/css/fonts/KaTeX_Main-Italic.woff 27.2 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/heaviside-step-graph-2.png 26.9 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/just-a-simple-lin-reg.png 26.6 kB
  • assets/css/fonts/KaTeX_Main-BoldItalic.woff 26.2 kB
  • Part 02-Module 03-Lesson 01_Model Selection/06. Detecting Overfitting and Underfitting with Learning Curves.html 26.2 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-25-at-11.35.38-am.png 25.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/max-pooling.png 25.8 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/weights-0-1-2.png 25.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.51.47-pm.png 24.9 kB
  • assets/css/fonts/KaTeX_Script-Regular.ttf 24.9 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/15. Linear Regression in scikit-learn.html 24.3 kB
  • assets/css/plyr.css 24.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/quadraticlinearregression.png 24.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. Perceptrons as Logical Operators.html 24.0 kB
  • assets/css/fonts/KaTeX_Math-Italic.woff 23.8 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/09. Quiz TensorFlow Linear Function.html 23.5 kB
  • assets/css/fonts/KaTeX_Fraktur-Bold.woff 23.4 kB
  • assets/css/fonts/KaTeX_Math-BoldItalic.woff 23.2 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/launch-instance.png 23.1 kB
  • assets/css/fonts/KaTeX_Main-Italic.woff2 23.1 kB
  • assets/css/fonts/KaTeX_Fraktur-Regular.woff 22.8 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/16. Implementing Backpropagation.html 22.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-10.05.46-pm.png 22.5 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/06. Training models in sklearn.html 22.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer Perceptrons.html 22.3 kB
  • assets/css/fonts/KaTeX_Main-BoldItalic.woff2 22.2 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/05. Perceptron.html 22.1 kB
  • assets/css/katex.min.css 22.1 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. Perceptrons as Logical Operators.html 21.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Algorithm.html 21.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/17. Decision Trees in sklearn.html 21.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/img/student-acceptance.png 21.0 kB
  • assets/css/fonts/KaTeX_Typewriter-Regular.woff 20.9 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/mnist-012.png 20.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation.html 20.7 kB
  • assets/css/fonts/KaTeX_Fraktur-Bold.woff2 20.5 kB
  • assets/css/fonts/KaTeX_Math-Italic.woff2 20.4 kB
  • assets/css/fonts/KaTeX_Math-BoldItalic.woff2 20.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/29. Mini Project Dermatologist AI.html 20.0 kB
  • assets/css/fonts/KaTeX_Fraktur-Regular.woff2 19.9 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/media/unnamed-project-desc-1.gif 19.6 kB
  • assets/css/fonts/KaTeX_Caligraphic-Bold.ttf 19.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Algorithm.html 19.5 kB
  • Part 10-Module 02-Lesson 06_Graphs/12. Graph Traversal Practice.html 19.5 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/04. Deadline Policy.html 19.4 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning.html 19.3 kB
  • assets/css/fonts/KaTeX_SansSerif-Bold.woff 19.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/30. Convolutional Network in TensorFlow.html 19.2 kB
  • assets/css/fonts/KaTeX_Caligraphic-Regular.ttf 19.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/07. Keras.html 18.1 kB
  • assets/css/fonts/KaTeX_SansSerif-Italic.woff 18.1 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/15. Exploration vs. Exploitation.html 17.8 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/img/two-layer-network.png 17.6 kB
  • assets/css/fonts/KaTeX_Typewriter-Regular.woff2 17.5 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/07. Tuning Parameters Manually.html 16.8 kB
  • assets/css/fonts/KaTeX_SansSerif-Regular.woff 16.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/14. Quiz Dimensionality.html 16.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/11. ReLU and Softmax Activation Functions.html 16.7 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/17. Multiple Linear Regression.html 16.6 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/10. Quiz Testing in sklearn.html 16.5 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/review-and-launch.png 16.1 kB
  • assets/css/fonts/KaTeX_SansSerif-Bold.woff2 16.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent.html 15.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/16. Visualizing CNNs.html 15.9 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/19. MC Control Constant-alpha, Part 2.html 15.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Refresh on Confusion Matrices.html 15.7 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/22. Summary.html 15.5 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/05. NumPy Arrays.html 15.3 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/17. SVMs in sklearn.html 15.3 kB
  • assets/css/fonts/KaTeX_SansSerif-Italic.woff2 15.2 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/16. Quiz One-Step Dynamics, Part 2.html 15.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/06. Save and Restore TensorFlow Models.html 15.0 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/14. Quiz Epsilon-Greedy Policies.html 15.0 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/25. Epochs.html 14.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/06. Filters.html 14.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/24. Visualizing CNNs (Part 2).html 14.7 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/img/dataframe.png 14.7 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/19. (Optional) Closed form Solution Math.html 14.6 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.pt-BR.vtt 14.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. Softmax.html 14.3 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/27. Summary.html 14.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/09. Parameters.html 14.1 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/04. Loading data into Pandas.html 14.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/05. Intuition.html 14.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/13. Quiz TensorFlow Dropout.html 14.0 kB
  • assets/css/fonts/KaTeX_SansSerif-Regular.woff2 14.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/08. Pre-Lab Student Admissions in Keras.html 14.0 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.en-US.vtt 13.9 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.en.vtt 13.9 kB
  • assets/css/fonts/KaTeX_Script-Regular.woff 13.9 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/aws-create-account.png 13.8 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent.html 13.8 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/09. The Simplest Neural Network.html 13.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/06. An Iterative Method, Part 2.html 13.6 kB
  • Part 05-Module 01-Lesson 07_Deep Learning Project/Project Rubric - Dog Breed Classifier.html 13.6 kB
  • Part 10-Module 02-Lesson 06_Graphs/08. Graph Representation Practice.html 13.6 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/Project Rubric - Resume Review Project (Career Change).html 13.5 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/Project Rubric - Resume Review Project (Entry-level).html 13.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-network.png 13.4 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/09. Quiz Goals and Rewards.html 13.3 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/Project Rubric - Resume Review Project (Prior Industry Experience).html 13.3 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/media/unnamed-59153-0.gif 13.3 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/quizimage.png 13.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs.html 13.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/img/screen-shot-2017-10-02-at-10.41.44-am.png 13.2 kB
  • assets/css/fonts/KaTeX_Size1-Regular.ttf 13.2 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/12. Quiz Optimal Policies.html 13.1 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/edit-security-group.png 13.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/09. Implementation.html 13.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-10.05.37-pm.png 13.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/04. Quiz Test Your Intuition.html 12.9 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/07. Quiz State-Value Functions.html 12.9 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/Project Rubric - Capstone Project.html 12.8 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/05. Launch an Instance.html 12.8 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/Project Rubric - LinkedIn Profile Review Project.html 12.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/16. Hyperparameters.html 12.7 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.51.51-pm.png 12.6 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/03. Quiz Interpret the Policy.html 12.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.pt-BR.vtt 12.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Neural Network Architecture.html 12.5 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/11. Action Values.html 12.4 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.en.vtt 12.4 kB
  • assets/css/fonts/KaTeX_Size2-Regular.ttf 12.4 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/19. Summary.html 12.4 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/07. Linked List Practice.html 12.3 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/21. Cross-Entropy 2.html 12.3 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/12. Quiz Pole-Balancing.html 12.3 kB
  • assets/css/fonts/KaTeX_Script-Regular.woff2 12.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/13. Convolutional Layers in Keras.html 12.2 kB
  • assets/css/fonts/KaTeX_Caligraphic-Bold.woff 12.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.pt-BR.vtt 12.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/27. Pre-Lab IMDB Data in Keras.html 12.0 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/31. TensorFlow Convolution Layer.html 12.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation.html 11.9 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.zh-CN.vtt 11.9 kB
  • assets/css/fonts/KaTeX_Caligraphic-Regular.woff 11.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/12. Gradient Descent The Code.html 11.8 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/04. Deep Neural Network in TensorFlow.html 11.8 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/07. Quiz An Iterative Method.html 11.8 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/index.jpg 11.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.en.vtt 11.6 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/img/screen-shot-2017-10-27-at-1.49.58-pm.png 11.6 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/img/career-portal-sidebar.png 11.6 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/img/career-portal-sidebar.png 11.6 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/img/career-portal-sidebar.png 11.6 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/img/career-portal-sidebar.png 11.6 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/career-portal-sidebar.png 11.6 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/img/career-portal-sidebar.png 11.6 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/img/career-portal-sidebar.png 11.6 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/career-portal-sidebar.png 11.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/img/career-portal-sidebar.png 11.6 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/img/career-portal-sidebar.png 11.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/img/career-portal-sidebar.png 11.6 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/Project Rubric - Creating Customer Segments.html 11.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Trick.html 11.6 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.pt-BR.vtt 11.6 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/18. Finite MDPs.html 11.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/neilsen-pic.png 11.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/08. (Optional) Margin Error Calculation.html 11.5 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/Project Rubric - Finding Donors for CharityML.html 11.4 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/15. Quiz One-Step Dynamics, Part 1.html 11.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. Refresh on ROC Curves.html 11.3 kB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/02. Instructions.html 11.3 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/13. Summary.html 11.3 kB
  • assets/css/fonts/KaTeX_Size4-Regular.ttf 11.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.pt-BR.vtt 11.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/08. Mini project Training an MLP on MNIST.html 11.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature Map Sizes.html 11.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.en.vtt 11.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/15. More on Sensitivity and Specificity.html 11.1 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/Project Description - ML Interview Practice.html 11.1 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.pt-BR.vtt 11.1 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.en.vtt 11.1 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/07. OR NOT Perceptron Quiz.html 11.1 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/08. XOR Perceptron Quiz.html 11.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/16. Max Pooling Layers in Keras.html 11.0 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.zh-CN.vtt 11.0 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/19. TensorFlow Max Pooling.html 11.0 kB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA.html 11.0 kB
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate.html 10.9 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/15. Categorical Cross-Entropy.html 10.9 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/13. Quiz TensorFlow Cross Entropy.html 10.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/10. Stack Practice.html 10.9 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/26. Check Your Understanding.html 10.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs for Image Classification.html 10.9 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Error vs Squared Error.html 10.8 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing your models.html 10.8 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/06. DDPG Agent.html 10.8 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two.html 10.8 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/img/smalldf.png 10.8 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations.html 10.7 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/Project Rubric - Predicting Boston Housing Prices.html 10.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/02. OpenAI Gym FrozenLakeEnv.html 10.7 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/Project Description - Capstone Project.html 10.7 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.pt-BR.vtt 10.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/07. Quiz Gaussian Mixtures.html 10.6 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/05. Python Practice.html 10.6 kB
  • assets/css/fonts/KaTeX_Caligraphic-Bold.woff2 10.6 kB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component of New System.html 10.6 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/11. F-beta Score.html 10.6 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. Solution Diagnosing Cancer.html 10.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/17. TensorFlow Convolution Layer.html 10.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/33. TensorFlow Pooling Layer.html 10.6 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/17. Summary.html 10.6 kB
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature.html 10.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.en.vtt 10.5 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/10. Quiz TensorFlow Softmax.html 10.5 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition.html 10.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.en.vtt 10.5 kB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data.html 10.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/06. AND Perceptron Quiz.html 10.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.en.vtt 10.4 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.en.vtt 10.4 kB
  • assets/css/fonts/KaTeX_Caligraphic-Regular.woff2 10.4 kB
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz.html 10.4 kB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features.html 10.4 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/24. Implementation.html 10.4 kB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance.html 10.3 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/07. Finetuning.html 10.3 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/Project Rubric - Udacity Professional Profile Review.html 10.3 kB
  • Part 05-Module 01-Lesson 06_Deep Learning Assessment/01. Assessment.html 10.3 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/05. Hello, Tensor World!.html 10.3 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/Project Rubric - ML Interview Practice.html 10.2 kB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information.html 10.2 kB
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System.html 10.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.en.vtt 10.2 kB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss.html 10.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/11. Solution Convolution Output Shape.html 10.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/03. Your Workspace.html 10.2 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/Project Rubric - Craft Your Cover Letter.html 10.1 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile.html 10.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood.html 10.1 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/04. Installing TensorFlow.html 10.1 kB
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers.html 10.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. Recommending Apps 1.html 10.1 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality.html 10.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/21. Implementation.html 10.1 kB
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality.html 10.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. Skin Cancer.html 10.1 kB
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz.html 10.1 kB
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data.html 10.1 kB
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System.html 10.1 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs (Part 1).html 10.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous.html 10.0 kB
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two.html 10.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/15. Implementation.html 10.0 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/04. DDPG Actor.html 10.0 kB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes.html 10.0 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.pt-BR.vtt 10.0 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.pt-BR.vtt 10.0 kB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality.html 10.0 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/01. Project Intro.html 10.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.zh-CN.vtt 10.0 kB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance.html 9.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.zh-CN.vtt 9.9 kB
  • Part 10-Module 02-Lesson 05_Trees/11. Binary Tree Practice.html 9.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/32. Solution TensorFlow Convolution Layer.html 9.9 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.pt-BR.vtt 9.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/22. Quiz Pooling Mechanics.html 9.9 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/05. Udacity Support.html 9.9 kB
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters.html 9.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/14. Quiz Parameter Sharing.html 9.8 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/11. Quiz Incremental Mean.html 9.8 kB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again).html 9.8 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.en-US.vtt 9.7 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.en.vtt 9.7 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.en.vtt 9.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/Project Description - Optimize Your GitHub Profile.html 9.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. Quiz Sensitivity and Specificity.html 9.7 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.en-US.vtt 9.7 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.en.vtt 9.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/08. Bayesian Learning 1.html 9.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.zh-CN.vtt 9.7 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/04. Implementation.html 9.7 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/06. Login to the Instance.html 9.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.en.vtt 9.7 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/10. Quiz Convolution Output Shape.html 9.7 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/12. Quiz Number of Parameters.html 9.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras.html 9.6 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/03. Quiz TensorFlow ReLUs.html 9.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/14. Project Description.html 9.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Trick.html 9.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example.html 9.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Maximizing Probabilities.html 9.6 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/11. Efficiency Practice.html 9.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/02. OpenAI Gym BlackjackEnv.html 9.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/10. Commit messages best practices.html 9.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.pt-BR.vtt 9.5 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/01. Using LinkedIn.html 9.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/26. Pre-Lab Gradient Descent.html 9.5 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.pt-BR.vtt 9.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/Project Rubric - Optimize Your GitHub Profile.html 9.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/34. Solution TensorFlow Pooling Layer.html 9.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. Logistic Regression.html 9.4 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/20. Quiz Pooling Intuition.html 9.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/14. Log-loss Error Function.html 9.4 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/05. DDPG Critic.html 9.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.pt-BR.vtt 9.4 kB
  • Part 10-Module 02-Lesson 05_Trees/14. BST Practice.html 9.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.en.vtt 9.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy.html 9.3 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/12. Implementation.html 9.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras.html 9.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/05. Recursion Practice.html 9.3 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.en.vtt 9.3 kB
  • Part 04-Module 07-Lesson 01_Unsupervised Learning Assessment/01. Assessment.html 9.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.pt-BR.vtt 9.3 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/06. Python The Basics.html 9.3 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.zh-CN.vtt 9.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. Quiz Data Challenges.html 9.3 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/05. Quiz Episodic or Continuing.html 9.3 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.zh-CN.vtt 9.3 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color.html 9.2 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/Project Description - Capstone Proposal.html 9.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images.html 9.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.en.vtt 9.2 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Write the Body.html 9.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. Quiz ROC Curve.html 9.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/24. Quiz Pooling Practice.html 9.2 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.en.vtt 9.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/27. Useful Resources.html 9.2 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/03. Python Dictionaries.html 9.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.en.vtt 9.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality.html 9.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. Quiz Random vs Pre-initialized Weights.html 9.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-60-2.png 9.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.pt-BR.vtt 9.2 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.pt-BR.vtt 9.1 kB
  • Part 03-Module 01-Lesson 07_Supervised Learning Assessment/01. Supervised Learning Assessment.html 9.1 kB
  • Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.ar.vtt 9.1 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/img/launch.png 9.1 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions.html 9.1 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work.html 9.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/35. CNNs - Additional Resources.html 9.1 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism.html 9.1 kB
  • Part 02-Module 03-Lesson 01_Model Selection/07. Solution Detecting Overfitting and Underfitting.html 9.1 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.en.vtt 9.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures.html 9.0 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/12. Next Steps.html 9.0 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/26. Quiz Average Pooling.html 9.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. Feedforward.html 9.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. Quiz Diagnosing Cancer.html 9.0 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish.html 9.0 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure.html 9.0 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain.html 9.0 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/12. Quiz Information Gain.html 9.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz.html 9.0 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/08. Troubleshooting.html 9.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.pt-BR.vtt 9.0 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/07. Implementation.html 9.0 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/10. String Keys Practice.html 9.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/20. Linear Regression Warnings.html 9.0 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/Project Rubric - Capstone Proposal.html 9.0 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/03. Customizing Your Profile.html 9.0 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/21. Solution Pooling Intuition.html 9.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/06. Higher Dimensions.html 8.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions continued.html 8.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/03. Classification Problems 1.html 8.9 kB
  • Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn.html 8.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/22. Multi-Class Cross Entropy.html 8.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.zh-CN.vtt 8.9 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.zh-CN.vtt 8.9 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/06. Transition to Classification.html 8.9 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/Project Rubric - Technical Interview Practice.html 8.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/07. Perceptrons.html 8.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.en.vtt 8.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.en.vtt 8.9 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project.html 8.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/23. Solution Pooling Mechanics.html 8.9 kB
  • Part 02-Module 04-Lesson 02_Model Evaluation and Validation Assessment/01. Model Evaluation and Validation assessment.html 8.8 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/06. Weighting the Models 2.html 8.8 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/08. Implementation.html 8.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro To CNNs.html 8.8 kB
  • Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.ar.vtt 8.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/12. Regularization.html 8.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/21. Mini project Image Augmentation in Keras.html 8.8 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/03. Get Access to GPU Instances.html 8.8 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling.html 8.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/15. Solution Parameter Sharing.html 8.8 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/16. Analyzing Performance.html 8.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. One-Hot Encoding.html 8.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/11. Multiclass Entropy.html 8.7 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.en.vtt 8.7 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Write the Introduction.html 8.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.pt-BR.vtt 8.7 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/13. Mini-batch Gradient Descent.html 8.7 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/02. Self-Practice Behavioral Questions.html 8.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/19. Mini project CNNs in Keras.html 8.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. GMM Examples Applications.html 8.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore The Design Space.html 8.6 kB
  • Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components.html 8.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories.html 8.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance.html 8.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks.html 8.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/13. Solution Number of Parameters.html 8.6 kB
  • Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation.html 8.6 kB
  • Part 06-Module 03-Lesson 01_Reinforcement Learning Assessment/01. Assessment.html 8.6 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix.html 8.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers (Part 2).html 8.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula 1.html 8.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions.html 8.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module.html 8.6 kB
  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation.html 8.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.zh-CN.vtt 8.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands).html 8.6 kB
  • Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA.html 8.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2.html 8.5 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/04. Python Lists.html 8.5 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. Image Challenges.html 8.5 kB
  • Part 04-Module 04-Lesson 01_PCA/26. Applying PCA to Real Data.html 8.5 kB
  • Part 10-Module 02-Lesson 06_Graphs/05. Graph Practice.html 8.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/22. Optimizers in Keras.html 8.5 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.zh-CN.vtt 8.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras.html 8.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs for Image Classification.html 8.5 kB
  • Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA.html 8.5 kB
  • Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code.html 8.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/27. Solution Average Pooling.html 8.5 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/07. Load Factor.html 8.5 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/02. OpenAI Gym CliffWalkingEnv.html 8.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization.html 8.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries.html 8.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/25. Solution Pooling Practice.html 8.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/11. Keep Practicing!.html 8.5 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/18. Implementation.html 8.5 kB
  • Part 05-Module 01-Lesson 07_Deep Learning Project/Project Description - Dog Breed Classifier.html 8.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.zh-CN.vtt 8.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.pt-BR.vtt 8.4 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.pt-BR.vtt 8.4 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/02. Resources.html 8.4 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.en.vtt 8.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Quiz Numerical Stability.html 8.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.pt-BR.vtt 8.4 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/10. [Quiz] Hierarchical clustering.html 8.4 kB
  • assets/css/fonts/KaTeX_Size3-Regular.ttf 8.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/02. Quiz Housing Prices.html 8.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/12. Mean vs Total Error.html 8.4 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/Project Rubric - Teach a Quadcopter How to Fly.html 8.3 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/Project Description - Creating Customer Segments.html 8.3 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt 8.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.pt-BR.vtt 8.3 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.zh-CN.vtt 8.3 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.en.vtt 8.3 kB
  • Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.pt-BR.vtt 8.3 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build.html 8.3 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/16. [Quiz] DBSCAN.html 8.3 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/04. Implementation.html 8.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.en.vtt 8.3 kB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/01. Introduction.html 8.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2.html 8.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/03. Binary Search Practice.html 8.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.en.vtt 8.2 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/06. False Negatives and Positives.html 8.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters.html 8.2 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/05. Quiz Student Admissions.html 8.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.en.vtt 8.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/25. Neural Networks Playground.html 8.2 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/Project Description - Technical Interview Practice.html 8.2 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/07. Projects.html 8.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/17. Resources in Your Career Portal.html 8.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means.html 8.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random vs Pre-initialized Weight.html 8.2 kB
  • Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.en.vtt 8.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/09. Lab Student Admissions in Keras.html 8.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction.html 8.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.ar.vtt 8.2 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.pt-BR.vtt 8.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-43.gif 8.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/27. Notebook Gradient Descent.html 8.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/28. Lab IMDB Data in Keras.html 8.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/09. Entropy Formula 2.html 8.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivity and Specificity.html 8.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What is the network looking at.html 8.1 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well .html 8.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.en.vtt 8.1 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.en.vtt 8.1 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/Project Description - Predicting Boston Housing Prices.html 8.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/28. Perceptron vs Gradient Descent.html 8.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example.html 8.1 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/18. Quiz Adjusted Rand Index.html 8.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions.html 8.1 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.pt-BR.vtt 8.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1.html 8.1 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/20. Implementation.html 8.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/03. Recommending Apps 2.html 8.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training the Neural Network.html 8.1 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.zh-CN.vtt 8.1 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/Project Description - Craft Your Cover Letter.html 8.1 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/06. Community Guidelines.html 8.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction.html 8.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges.html 8.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating the Training.html 8.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers.html 8.0 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2.html 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification.html 8.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3.html 8.0 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Quiz.html 8.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition).html 8.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric.html 8.0 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/04. Top Section.html 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Probability of Skin Cancer.html 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve.html 8.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.pt-BR.vtt 8.0 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/02. Description.html 8.0 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. Internal Validation Indices.html 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. Comparing our Results with Doctors.html 8.0 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.pt-BR.vtt 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix.html 8.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/10. Mini Project DP (Parts 0 and 1).html 8.0 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/16. Implementation.html 8.0 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.en.vtt 8.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/13. Mini Project DP (Part 2).html 8.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/16. Mini Project DP (Part 3).html 8.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/19. Mini Project DP (Part 4).html 8.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/22. Mini Project DP (Part 5).html 8.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/25. Mini Project DP (Part 6).html 8.0 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/11. Time for Live Practice with Pramp.html 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization.html 8.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis.html 8.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/20. Cross-Entropy 1.html 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Intro.html 8.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion.html 8.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters.html 7.9 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/07. Resume Review (Prior Industry Experience).html 7.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The data.html 7.9 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/09. Bubble Sort Practice.html 7.9 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. External Validation Indices.html 7.9 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/12. Merge Sort Practice.html 7.9 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/14. Implementation.html 7.9 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. Overview of The Expectation Maximization (EM) Algorithm.html 7.9 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/03. Software and Data Requirements.html 7.9 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/07. Resume Review (Career Change).html 7.9 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/22. GMM Cluster Validation Lab Solution.html 7.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent.html 7.8 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/12. Queue Practice.html 7.8 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/21. GMM Cluster Validation Lab.html 7.8 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.zh-CN.vtt 7.8 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages.html 7.8 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.pt-BR.vtt 7.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World.html 7.8 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/11. Naive Bayes Algorithm 1.html 7.8 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.en-US.vtt 7.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Quiz.html 7.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/01. Announcement.html 7.8 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. Practical Aspects of Learning.html 7.8 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/05. Example Reports.html 7.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers (Part 1).html 7.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/07. Resume Review (Entry-level).html 7.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.zh-CN.vtt 7.8 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1.html 7.8 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2.html 7.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/25. Logistic Regression Algorithm.html 7.8 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.en.vtt 7.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity.html 7.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding.html 7.8 kB
  • Part 10-Module 02-Lesson 05_Trees/04. Tree Practice.html 7.8 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs and Initial Weights .html 7.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Problems 2.html 7.8 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.pt-BR.vtt 7.8 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. Optimizing a Logistic Classifier.html 7.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons.html 7.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.pt-BR.vtt 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. Gaussian Mixture Model (GMM) Clustering.html 7.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. Momentum and Learning Rate Decay.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Early Stopping.html 7.7 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/12. Polynomial Kernel 2.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier .html 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. Expectation Maximization Part 1.html 7.7 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient.html 7.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big and Small .html 7.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/06. Naive Bayes Quiz.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models.html 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. Expectation Maximization Part 2.html 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. Visual Example of EM Progress.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. Stochastic Gradient Descent.html 7.7 kB
  • Part 02-Module 03-Lesson 01_Model Selection/09. Grid Search in sklearn.html 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. Gaussian Distribution in One Dimension.html 7.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-linear Data.html 7.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate Decay.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization 2.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. Minimizing Cross Entropy.html 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. Gaussian Distribution in 2D.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart.html 7.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.en.vtt 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima.html 7.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Mini Project Intro.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. Measuring Performance .html 7.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/18. Kernel Method Quiz.html 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. Cluster Analysis Process.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification.html 7.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. GMM Clustering in One Dimension.html 7.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. Parameter Hyperspace .html 7.7 kB
  • Part 02-Module 04-Lesson 01_NumPy and pandas Assessment/01. Assessment.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Outro.html 7.7 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/09. Recall.html 7.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum.html 7.7 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/03. Software and Data Requirements.html 7.7 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/Project Description - Finding Donors for CharityML.html 7.6 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout.html 7.6 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What is Deep Learning .html 7.6 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let's Get Started .html 7.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/12. Quiz Expectation Maximization.html 7.6 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.en.vtt 7.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. GMM Implementation.html 7.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. Cluster Validation.html 7.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. Intro.html 7.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/05. Mini Project MC (Parts 0 and 1).html 7.6 kB
  • Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.ar.vtt 7.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation.html 7.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration.html 7.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/08. Mini Project MC (Part 2).html 7.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/17. Mini Project MC (Part 3).html 7.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/21. Mini Project MC (Part 4).html 7.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/01. Introduction.html 7.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.pt-BR.vtt 7.6 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components.html 7.6 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/06. Quiz False Positives.html 7.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. GMM in 2D.html 7.6 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron.html 7.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/20. Quiz Silhouette Coefficient .html 7.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/13. Resources in Your Career Portal.html 7.6 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/03. Decision Trees Quiz.html 7.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method, Part 1.html 7.6 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.pt-BR.vtt 7.6 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/02. Which line is better.html 7.6 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression Quiz.html 7.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects.html 7.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement.html 7.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration.html 7.5 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration.html 7.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/14. Support Vector Machines Quiz.html 7.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return.html 7.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression.html 7.5 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation.html 7.5 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.pt-BR.vtt 7.5 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.pt-BR.vtt 7.5 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.en.vtt 7.5 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.en.vtt 7.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/13. Solution Information Gain.html 7.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/12. Resources in Your Career Portal.html 7.5 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/06. Submitting the Project.html 7.5 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/08. Precision.html 7.5 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.zh-CN.vtt 7.5 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.pt-BR.vtt 7.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris.html 7.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/29. Outro.html 7.5 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/07. Ornstein–Uhlenbeck Noise.html 7.5 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt 7.5 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.zh-CN.vtt 7.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items.html 7.5 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/04. Quiz Space Representations.html 7.5 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. HC examples and applications.html 7.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction.html 7.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter.html 7.5 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.pt-BR.vtt 7.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.pt-BR.vtt 7.4 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.pt-BR.vtt 7.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/13. Building a Spam Classifier.html 7.4 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.en.vtt 7.4 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction.html 7.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.en.vtt 7.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.en.vtt 7.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.en.vtt 7.4 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.zh-CN.vtt 7.4 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. DBSCAN examples applications.html 7.4 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2.html 7.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions.html 7.4 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.zh-CN.vtt 7.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.pt-BR.vtt 7.3 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/01. Overview.html 7.3 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.en.vtt 7.3 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications.html 7.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/02. Random Projection.html 7.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2.html 7.3 kB
  • Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph.html 7.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.pt-BR.vtt 7.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.pt-BR.vtt 7.3 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/18. Titanic Survival Model with Decision Trees.html 7.3 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/11. Implementation.html 7.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2.html 7.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement.html 7.3 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula 3.html 7.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation.html 7.2 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/19. [Solution] Titanic Survival Model.html 7.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration.html 7.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha, Part 1.html 7.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values.html 7.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean.html 7.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values.html 7.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/16. Outro.html 7.2 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.en.vtt 7.2 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/08. Resources in Your Career Portal.html 7.2 kB
  • Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.ar.vtt 7.2 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/01. Overview.html 7.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution.html 7.2 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion.html 7.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1.html 7.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2.html 7.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3.html 7.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.pt-BR.vtt 7.2 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/06. ICA.html 7.2 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization Quiz.html 7.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.zh-CN.vtt 7.2 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer.html 7.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository.html 7.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.pt-BR.vtt 7.2 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. DBSCAN.html 7.2 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer.html 7.2 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering.html 7.2 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.en.vtt 7.2 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/05. Weighting the Models 1.html 7.1 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer.html 7.1 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/08. Resources in Your Career Portal.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer.html 7.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.zh-CN.vtt 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge.html 7.1 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/08. Resources in Your Career Portal.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method.html 7.1 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.pt-BR.vtt 7.1 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.en.vtt 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/01. What Is Machine Learning.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/15. Support Vector Machines Answer.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. Support Vector Machines.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes.html 7.1 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/11. Implementing Deep Q-Learning.html 7.1 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/05. Perceptron Algorithm.html 7.1 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/08. [Lab Solution] Hierarchical Clustering.html 7.1 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes #1.html 7.1 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/02. Software Requirements.html 7.1 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/09. Resources in Your Career Portal.html 7.1 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.en.vtt 7.1 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/10. Quiz Action-Value Functions.html 7.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.zh-CN.vtt 7.1 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/07. [Lab] Hierarchical clustering .html 7.1 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/01. Overview.html 7.1 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices.html 7.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/23. Summary.html 7.1 kB
  • Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.en-US.vtt 7.1 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/07. More Resources.html 7.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/06. Solution Student Admissions.html 7.0 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/08. Resources in Your Career Portal.html 7.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/14. [Lab Solution] DBSCAN.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression.html 7.0 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/01. Intro.html 7.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/13. [Lab] DBSCAN.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting a Line Through Data.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error.html 7.0 kB
  • Part 10-Module 02-Lesson 05_Trees/07. Tree Traversal Practice.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions.html 7.0 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/01. Random Projection.html 7.0 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/09. AdaBoost in sklearn.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent.html 7.0 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.en-US.vtt 7.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. Hierarchical clustering implementation.html 7.0 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.en.vtt 7.0 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements.html 7.0 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/02. Software Requirements.html 7.0 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/03. Your Udacity Professional Profile.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization.html 7.0 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/03. Personal Branding.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/05. Moving a Line.html 7.0 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/05. Recruitment Data.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick.html 7.0 kB
  • assets/css/fonts/KaTeX_Size1-Regular.woff 7.0 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.en.vtt 7.0 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.zh-CN.vtt 7.0 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/15. Quick Sort Practice.html 7.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/23. Outro.html 7.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. Examining single-link clustering.html 7.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. Complete-link, average-link, Ward.html 7.0 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/05. Mini Project TD (Parts 0 and 1).html 6.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.pt-BR.vtt 6.9 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/01. Overview.html 6.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction.html 6.9 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/09. Mini Project TD (Part 2).html 6.9 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/12. Mini Project TD (Part 3).html 6.9 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/15. Mini Project TD (Part 4).html 6.9 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. Higher Dimensions.html 6.9 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classification Problems 1.html 6.9 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.pt-BR.vtt 6.9 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.zh-CN.vtt 6.9 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/Project Description - Udacity Professional Profile Review.html 6.9 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/02. Course Outline.html 6.9 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.zh-CN.vtt 6.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent The Math.html 6.9 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/04. Submitting the project.html 6.9 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.en.vtt 6.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/10. F1 Score.html 6.9 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. DBSCAN implementation.html 6.9 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/15. Random Forests.html 6.9 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/10. ICA Applications.html 6.9 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/04. Submitting the project.html 6.9 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/04. Report Guidelines.html 6.9 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions.html 6.9 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/03. Submitting the project.html 6.9 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain.html 6.9 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. Perceptrons.html 6.9 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/14. Project.html 6.8 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch.html 6.8 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It.html 6.8 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills.html 6.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.zh-CN.vtt 6.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/Project Description - Resume Review Project (Entry-level).html 6.8 kB
  • Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3.html 6.8 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/04. Apply Credits.html 6.8 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/02. Description.html 6.8 kB
  • Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.en.vtt 6.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps 3.html 6.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/01. Intro.html 6.8 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks.html 6.8 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/03. Starting the project.html 6.8 kB
  • Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion.html 6.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/diagonal-line-2.png 6.8 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/diagonal-line-2.png 6.8 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/04. Proposal Guidelines.html 6.8 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/Project Description - Resume Review Project (Prior Industry Experience).html 6.8 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. Overview of other clustering methods.html 6.8 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. Hierarchical clustering single-link.html 6.8 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values.html 6.8 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1.html 6.8 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format.html 6.8 kB
  • Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.pt-BR.vtt 6.8 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.zh-CN.vtt 6.8 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/03. Starting the project.html 6.8 kB
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means.html 6.8 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/15. Spam Classifier - Workspace.html 6.7 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.pt-BR.vtt 6.7 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.en.vtt 6.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited.html 6.7 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. K-means considerations.html 6.7 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up.html 6.7 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.en.vtt 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals.html 6.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis.html 6.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/17. Further Reading.html 6.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy.html 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees.html 6.7 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/08. Resources in Your Career Portal.html 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees.html 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation.html 6.7 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.en.vtt 6.7 kB
  • Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning.html 6.7 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax.html 6.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/20. Outro.html 6.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward.html 6.7 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa.html 6.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/10. Bayesian Learning 3.html 6.7 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.en.vtt 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/08. Search and Delete.html 6.7 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/02. Starting the project.html 6.7 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/03. Replay Buffer.html 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology.html 6.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.pt-BR.vtt 6.7 kB
  • assets/css/fonts/KaTeX_Size2-Regular.woff 6.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/04. Guess the Person Now.html 6.7 kB
  • Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies.html 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal.html 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations.html 6.7 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/05. Uploading to Workspace.html 6.7 kB
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie.html 6.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2.html 6.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3.html 6.7 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review.html 6.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.pt-BR.vtt 6.7 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/Project Description - Resume Review Project (Career Change).html 6.7 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning.html 6.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.en.vtt 6.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.pt-BR.vtt 6.7 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm.html 6.7 kB
  • Part 10-Module 02-Lesson 05_Trees/02. Tree Basics.html 6.6 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/05. Uploading to Workspace.html 6.6 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.en.vtt 6.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.pt-BR.vtt 6.6 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression Answer.html 6.6 kB
  • Part 10-Module 02-Lesson 05_Trees/16. Heapify.html 6.6 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.en.vtt 6.6 kB
  • Part 10-Module 02-Lesson 05_Trees/09. Insert.html 6.6 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.en.vtt 6.6 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction.html 6.6 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/04. Uploading to Workspace.html 6.6 kB
  • Part 10-Module 02-Lesson 05_Trees/01. Trees.html 6.6 kB
  • Part 10-Module 02-Lesson 05_Trees/15. Heaps.html 6.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/13. Sklearn.html 6.6 kB
  • Part 10-Module 02-Lesson 05_Trees/12. BSTs.html 6.6 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/02. Getting Started.html 6.6 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review.html 6.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.pt-BR.vtt 6.6 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review.html 6.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.pt-BR.vtt 6.6 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/02. Two-Layer Neural Network.html 6.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question.html 6.6 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/z93yz2vrgdaacqjowbaabie8yaaackcwmaaadshdeaaabpwhgaaia0yqwaaecamayaacbngamaajamjaeaaegtxgaaakqjywaaan 6.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction.html 6.6 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.zh-CN.vtt 6.6 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/03. Minimizing Distances.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/06. Classification Error.html 6.5 kB
  • Part 02-Module 03-Lesson 01_Model Selection/11. [Solution] Grid Search Lab.html 6.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.zh-CN.vtt 6.5 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/01. Introduction.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/11. Polynomial Kernel 1.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/13. Polynomial Kernel 3.html 6.5 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/03. STAR Method.html 6.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/03. Analyzing an Interview.html 6.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis.html 6.5 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.pt-BR.vtt 6.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/index.html 6.5 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/07. Program Readiness.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/04. Error Function Intuition.html 6.5 kB
  • Part 02-Module 03-Lesson 01_Model Selection/10. Grid Search Lab.html 6.5 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs.html 6.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/10. The C Parameter.html 6.5 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/05. Submitting the Project.html 6.5 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis.html 6.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/09. Error Function.html 6.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/12. TensorFlow Implementation.html 6.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.pt-BR.vtt 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Intro.html 6.5 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/07. Margin Error.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/14. RBF Kernel 1.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/15. RBF Kernel 2.html 6.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/16. RBF Kernel 3.html 6.5 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!.html 6.5 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/04. Independent Component Analysis (ICA).html 6.5 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming.html 6.5 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/06. Exercise Discretization.html 6.5 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/Project Description - LinkedIn Profile Review Project.html 6.5 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative.html 6.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.pt-BR.vtt 6.5 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.ar.vtt 6.5 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/03. Analyzing Behavioral Answers.html 6.5 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/08. Exercise Tile Coding.html 6.5 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/05. Resources in Your Career Portal.html 6.5 kB
  • Part 10-Module 02-Lesson 05_Trees/13. BST Complications.html 6.5 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases.html 6.5 kB
  • assets/css/fonts/KaTeX_Size4-Regular.woff 6.5 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging.html 6.5 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries.html 6.4 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.en.vtt 6.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. Naive Bayes Algorithm 2.html 6.4 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation.html 6.4 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding.html 6.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace.html 6.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure.html 6.4 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.zh-CN.vtt 6.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.zh-CN.vtt 6.4 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.en.vtt 6.4 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search.html 6.4 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences.html 6.4 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/02. Resources in Your Career Portal.html 6.4 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/01. Overview.html 6.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/09. Bayesian Learning 2.html 6.4 kB
  • Part 04-Module 04-Lesson 01_PCA/index.html 6.4 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort.html 6.4 kB
  • Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.en.vtt 6.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/07. Solution False Positives.html 6.4 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences.html 6.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company.html 6.4 kB
  • Part 01-Module 01-Lesson 03_Introductory Practice Project/03. Project files.html 6.4 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort.html 6.4 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort.html 6.4 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay.html 6.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/03. Known and Inferred.html 6.4 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0).html 6.4 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0).html 6.4 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/Project Description - Teach a Quadcopter How to Fly.html 6.4 kB
  • Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.pt-BR.vtt 6.4 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn.html 6.4 kB
  • Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search.html 6.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.pt-BR.vtt 6.4 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro.html 6.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/18. Outro.html 6.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/02. Guess the Person.html 6.4 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/06. Skills.html 6.4 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network.html 6.4 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/01. Project Overview.html 6.4 kB
  • Part 01-Module 01-Lesson 03_Introductory Practice Project/02. Software Requirements.html 6.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.pt-BR.vtt 6.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/05. Bayes Theorem.html 6.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/01. Introduction.html 6.3 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning.html 6.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting.html 6.3 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/10. Arpan's Analysis of the Interview.html 6.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt 6.3 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro.html 6.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt 6.3 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning.html 6.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search.html 6.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/01. Intro.html 6.3 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets.html 6.3 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation.html 6.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort.html 6.3 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.en.vtt 6.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/16. Outro.html 6.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort.html 6.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort.html 6.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/09. [Solution] Independent Component Analysis.html 6.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion.html 6.3 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces.html 6.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.pt-BR.vtt 6.3 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team.html 6.3 kB
  • Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves.html 6.3 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation.html 6.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/08. [Lab] Independent Component Analysis.html 6.3 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. Welcome to the Machine Learning Engineer Nanodegree Program.html 6.3 kB
  • Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.en-US.vtt 6.3 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.en.vtt 6.3 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.zh-CN.vtt 6.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt 6.3 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely.html 6.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt 6.2 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.es-MX.vtt 6.2 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction.html 6.2 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components.html 6.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.en.vtt 6.2 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-05-22-at-12.25.34-pm.png 6.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.pt-BR.vtt 6.2 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction.html 6.2 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/perceptron-equation-2.gif 6.2 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Precision and Recall.html 6.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt 6.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.en.vtt 6.2 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.pt-BR.vtt 6.2 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network.html 6.2 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.zh-CN.vtt 6.2 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/02. Quadcopter workspace.html 6.2 kB
  • Part 02-Module 03-Lesson 01_Model Selection/03. Cross Validation.html 6.2 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.zh-CN.vtt 6.2 kB
  • Part 11-Module 01-Lesson 01_Software and Tools/01. TensorFlow.html 6.2 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions.html 6.2 kB
  • Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.zh-CN.vtt 6.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.zh-CN.vtt 6.2 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely.html 6.2 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/02. Access Your Career Portal.html 6.2 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection.html 6.2 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization.html 6.2 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network.html 6.2 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components.html 6.2 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure.html 6.2 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding.html 6.1 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions.html 6.1 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely.html 6.1 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components.html 6.1 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.en.vtt 6.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.pt-BR.vtt 6.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.pt-BR.vtt 6.1 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.pt-BR.vtt 6.1 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding.html 6.1 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning.html 6.1 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.en.vtt 6.1 kB
  • Part 02-Module 03-Lesson 01_Model Selection/01. Types of Errors.html 6.1 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.zh-CN.vtt 6.1 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.zh-CN.vtt 6.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/index.html 6.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.zh-CN.vtt 6.1 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.en-US.vtt 6.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.en.vtt 6.1 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary.html 6.1 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning.html 6.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2.html 6.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2.html 6.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion Matrix 2.html 6.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles.html 6.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations.html 6.1 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.pt-BR.vtt 6.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2.html 6.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization.html 6.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout.html 6.1 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection.html 6.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction.html 6.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices.html 6.1 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.pt-BR.vtt 6.1 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure.html 6.1 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection.html 6.1 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys.html 6.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph.html 6.1 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure.html 6.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal.html 6.0 kB
  • Part 02-Module 03-Lesson 01_Model Selection/13. Outro.html 6.0 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/06. Project Workspace.html 6.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path.html 6.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt 6.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt 6.0 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/02. Create an AWS Account.html 6.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/04. Connectivity.html 6.0 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris.html 6.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example.html 6.0 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/08. Experience.html 6.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies.html 6.0 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/05. FastICA Algorithm.html 6.0 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/06. Workspace.html 6.0 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.en.vtt 6.0 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up.html 6.0 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.pt-BR.vtt 6.0 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.pt-BR.vtt 6.0 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources.html 6.0 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Intro to Deep Neural Networks.html 6.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality.html 6.0 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/05. Project Workspace.html 6.0 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/01. Introduction.html 6.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies.html 6.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/10. DFS.html 6.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/11. BFS.html 6.0 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.pt-BR.vtt 6.0 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.zh-CN.vtt 5.9 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.en-US.vtt 5.9 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose of the Cover Letter.html 5.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections.html 5.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt 5.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression Metrics.html 5.9 kB
  • Part 02-Module 03-Lesson 01_Model Selection/12. Summary.html 5.9 kB
  • Part 04-Module 02-Lesson 02_Clustering Mini-Project/01. Intro.html 5.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth.html 5.9 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.pt-BR.vtt 5.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/index.html 5.9 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.zh-CN.vtt 5.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.pt-BR.vtt 5.9 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction.html 5.9 kB
  • Part 02-Module 03-Lesson 01_Model Selection/04. K-Fold Cross Validation.html 5.9 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued.html 5.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/perceptron-formula.gif 5.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/diagonal-line-1.png 5.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/diagonal-line-1.png 5.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details.html 5.9 kB
  • Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.pt-BR.vtt 5.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve.html 5.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists.html 5.9 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/10. Resources.html 5.9 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.zh-CN.vtt 5.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.en.vtt 5.9 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/03. Course Expectations.html 5.9 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.zh-CN.vtt 5.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.zh-CN.vtt 5.8 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps.html 5.8 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt 5.8 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When accuracy won't work.html 5.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.en.vtt 5.8 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency.html 5.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.en.vtt 5.8 kB
  • Part 01-Module 01-Lesson 03_Introductory Practice Project/01. Overview.html 5.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.zh-CN.vtt 5.8 kB
  • assets/css/fonts/KaTeX_Size1-Regular.woff2 5.8 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms.html 5.8 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.zh-CN.vtt 5.8 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter.html 5.8 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays.html 5.8 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks.html 5.8 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues.html 5.8 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/03. Stats Refresher.html 5.8 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax.html 5.8 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists.html 5.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.pt-BR.vtt 5.8 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.en-US.vtt 5.8 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.en.vtt 5.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.pt-BR.vtt 5.8 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing.html 5.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/index.html 5.8 kB
  • Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.ar.vtt 5.8 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/index.html 5.8 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/03. Random Projection in sklearn.html 5.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/index.html 5.8 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/08. Tuning Parameters Automatically.html 5.8 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps.html 5.8 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem.html 5.8 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Why Use Elevator Pitches.html 5.8 kB
  • Part 11-Module 01-Lesson 02_Deep Learning/03. Deep Learning What You'll Do.html 5.8 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm.html 5.8 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.zh-CN.vtt 5.8 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.pt-BR.vtt 5.8 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction.html 5.8 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.zh-CN.vtt 5.8 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.en.vtt 5.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/inputs-matrix.png 5.7 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.pt-BR.vtt 5.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt 5.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt 5.7 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch.html 5.7 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.en.vtt 5.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.en.vtt 5.7 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem.html 5.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Problems 2.html 5.7 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/07. Weighting the Models 3.html 5.7 kB
  • Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.zh-CN.vtt 5.7 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.zh-CN.vtt 5.7 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/07. ICA in sklearn.html 5.7 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps.html 5.7 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming.html 5.7 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/08. Combining the Models.html 5.7 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm.html 5.7 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/04. Weighting the Data.html 5.7 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.pt-BR.vtt 5.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Intro.html 5.7 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions.html 5.7 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem.html 5.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.zh-CN.vtt 5.7 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.zh-CN.vtt 5.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/index.html 5.7 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing.html 5.7 kB
  • Part 11-Module 01-Lesson 02_Deep Learning/01. Deep Learning.html 5.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/index.html 5.7 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/01. Intro.html 5.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.en.vtt 5.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/10. Outro.html 5.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.pt-BR.vtt 5.7 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt 5.6 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.en-US.vtt 5.6 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/03. AdaBoost.html 5.6 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/02. Bagging.html 5.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.en.vtt 5.6 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. Actor-Critic with Advantage.html 5.6 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/01. Intro.html 5.6 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/06. Reference Guide.html 5.6 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/11. Outro.html 5.6 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.pt-BR.vtt 5.6 kB
  • Part 05-Module 01-Lesson 07_Deep Learning Project/02. Dog Breed Workspace.html 5.6 kB
  • Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.pt-BR.vtt 5.6 kB
  • Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.zh-CN.vtt 5.6 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. Actor-Critic Methods.html 5.6 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/02. Outline.html 5.6 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. Two Function Approximators.html 5.6 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.pt-BR.vtt 5.6 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer.html 5.6 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. Advantage Function.html 5.6 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction.html 5.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.en.vtt 5.6 kB
  • assets/css/fonts/KaTeX_Size2-Regular.woff2 5.6 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. The Actor and The Critic.html 5.6 kB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.ar.vtt 5.6 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.en.vtt 5.6 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. A Better Score Function.html 5.6 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/08. Week 1 Plan.html 5.5 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.pt-BR.vtt 5.5 kB
  • Part 01-Module 01-Lesson 03_Introductory Practice Project/04. Titanic Survival Exploration.html 5.5 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.zh-CN.vtt 5.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.zh-CN.vtt 5.5 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/09. Week 2 Plan.html 5.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.zh-CN.vtt 5.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.en.vtt 5.5 kB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/03. Mini Project.html 5.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.en.vtt 5.5 kB
  • Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.en.vtt 5.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.zh-CN.vtt 5.5 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity.html 5.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.en.vtt 5.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.en.vtt 5.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.en.vtt 5.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.pt-BR.vtt 5.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.en.vtt 5.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.pt-BR.vtt 5.5 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.zh-CN.vtt 5.5 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.en.vtt 5.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.zh-CN.vtt 5.5 kB
  • Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.pt-BR.vtt 5.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.ar.vtt 5.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.zh-CN.vtt 5.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.en.vtt 5.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.pt-BR.vtt 5.4 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit.html 5.4 kB
  • Part 04-Module 02-Lesson 02_Clustering Mini-Project/02. K-means clustering of movie ratings.html 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/03. Policy Function Approximation.html 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/06. Monte Carlo Policy Gradients.html 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/07. Constrained Policy Gradients.html 5.4 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.pt-BR.vtt 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/02. Why Policy-Based Methods.html 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/04. Stochastic Policy Search.html 5.4 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/index.html 5.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.pt-BR.vtt 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/01. Policy-Based Methods.html 5.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.en.vtt 5.4 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara.html 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/05. Policy Gradients.html 5.4 kB
  • Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.en.vtt 5.4 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset.html 5.4 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.en.vtt 5.4 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales.html 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/08. Recap.html 5.4 kB
  • Part 04-Module 02-Lesson 02_Clustering Mini-Project/03. Solution.html 5.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.pt-BR.vtt 5.4 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.zh-CN.vtt 5.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.pt-BR.vtt 5.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.zh-CN.vtt 5.4 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary.html 5.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/index.html 5.4 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction.html 5.4 kB
  • Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.zh-CN.vtt 5.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.pt-BR.vtt 5.4 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/index.html 5.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/index.html 5.3 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.pt-BR.vtt 5.3 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.zh-CN.vtt 5.3 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.es-MX.vtt 5.3 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.pt-BR.vtt 5.3 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.zh-CN.vtt 5.3 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.pt-BR.vtt 5.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.zh-CN.vtt 5.3 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.en.vtt 5.3 kB
  • Part 11-Module 01-Lesson 02_Deep Learning/02. What You'll Watch and Learn.html 5.3 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails.html 5.3 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.ar.vtt 5.3 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.ar.vtt 5.3 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.pt-BR.vtt 5.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.pt-BR.vtt 5.2 kB
  • Part 04-Module 05-Lesson 01_PCA Mini-Project/01. PCA Mini-Project.html 5.2 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.en.vtt 5.2 kB
  • assets/css/fonts/KaTeX_Size4-Regular.woff2 5.2 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.pt-BR.vtt 5.2 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviews are Conversations.html 5.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.pt-BR.vtt 5.2 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting.html 5.2 kB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer.html 5.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.zh-CN.vtt 5.2 kB
  • Part 05-Module 01-Lesson 07_Deep Learning Project/01. Dog Breed Recognition Project.html 5.1 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.en.vtt 5.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.en-US.vtt 5.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.en-US.vtt 5.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.en.vtt 5.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.en.vtt 5.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.pt-BR.vtt 5.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.zh-CN.vtt 5.1 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt 5.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt 5.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/index.html 5.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.zh-CN.vtt 5.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.zh-CN.vtt 5.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.zh-CN.vtt 5.1 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.zh-CN.vtt 5.1 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/index.html 5.0 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/index.html 5.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.zh-CN.vtt 5.0 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.en.vtt 5.0 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.en.vtt 5.0 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.en.vtt 5.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.en.vtt 5.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.ar.vtt 5.0 kB
  • Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.zh-CN.vtt 5.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/index.html 4.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.en.vtt 4.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.pt-BR.vtt 4.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.en.vtt 4.9 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.ar.vtt 4.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/index.html 4.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.zh-CN.vtt 4.9 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.en.vtt 4.9 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.en.vtt 4.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.zh-CN.vtt 4.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.en.vtt 4.9 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.zh-CN.vtt 4.9 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/index.html 4.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.pt-BR.vtt 4.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.pt-BR.vtt 4.9 kB
  • Part 10-Module 02-Lesson 05_Trees/index.html 4.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.zh-CN.vtt 4.9 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.pt-BR.vtt 4.8 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.en-US.vtt 4.8 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/index.html 4.8 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.zh-CN.vtt 4.8 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.en.vtt 4.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt 4.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.en.vtt 4.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.zh-CN.vtt 4.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.zh-CN.vtt 4.8 kB
  • Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.zh-CN.vtt 4.8 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/index.html 4.8 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.zh-CN.vtt 4.8 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.en.vtt 4.8 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.zh-CN.vtt 4.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.zh-CN.vtt 4.8 kB
  • assets/css/fonts/KaTeX_Size3-Regular.woff 4.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.pt-BR.vtt 4.8 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/index.html 4.8 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.zh-CN.vtt 4.8 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.en.vtt 4.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.zh-CN.vtt 4.8 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.en.vtt 4.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt 4.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.zh-CN.vtt 4.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.pt-BR.vtt 4.7 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.zh-CN.vtt 4.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.zh-CN.vtt 4.7 kB
  • Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.ar.vtt 4.7 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.en.vtt 4.7 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/index.html 4.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/index.html 4.7 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.pt-BR.vtt 4.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.ar.vtt 4.7 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.pt-BR.vtt 4.7 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.en.vtt 4.7 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.en.vtt 4.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.pt-BR.vtt 4.7 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.en.vtt 4.7 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.pt-BR.vtt 4.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.pt-BR.vtt 4.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.pt-BR.vtt 4.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.zh-CN.vtt 4.6 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/index.html 4.6 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.pt-BR.vtt 4.6 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/index.html 4.6 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.zh-CN.vtt 4.6 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.es-MX.vtt 4.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.zh-CN.vtt 4.6 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.pt-BR.vtt 4.6 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/index.html 4.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.en.vtt 4.6 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.en.vtt 4.6 kB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.pt-BR.vtt 4.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.pt-BR.vtt 4.6 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.en.vtt 4.6 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/index.html 4.6 kB
  • Part 02-Module 03-Lesson 01_Model Selection/index.html 4.6 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.pt-BR.vtt 4.6 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.pt-BR.vtt 4.6 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.zh-CN.vtt 4.6 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.pt-BR.vtt 4.6 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.ar.vtt 4.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.pt-BR.vtt 4.6 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.en.vtt 4.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/index.html 4.5 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.en-US.vtt 4.5 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.en.vtt 4.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.zh-CN.vtt 4.5 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/index.html 4.5 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.zh-CN.vtt 4.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.en.vtt 4.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.en.vtt 4.5 kB
  • Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.pt-BR.vtt 4.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.zh-CN.vtt 4.5 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/index.html 4.5 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/index.html 4.5 kB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.en.vtt 4.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.ar.vtt 4.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.en.vtt 4.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.pt-BR.vtt 4.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.en.vtt 4.5 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.pt-BR.vtt 4.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.zh-CN.vtt 4.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.ar.vtt 4.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.zh-CN.vtt 4.4 kB
  • Part 09-Module 01-Lesson 03_Udacity Professional Profile/index.html 4.4 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.pt-BR.vtt 4.4 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/index.html 4.4 kB
  • Part 10-Module 02-Lesson 06_Graphs/index.html 4.4 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/index.html 4.4 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.pt-BR.vtt 4.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.pt-BR.vtt 4.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-05-22-at-12.27.55-pm.png 4.4 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.pt-BR.vtt 4.4 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.en.vtt 4.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt 4.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/softmax-math.png 4.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.zh-CN.vtt 4.4 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/index.html 4.4 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.pt-BR.vtt 4.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.en.vtt 4.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.pt-BR.vtt 4.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.pt-BR.vtt 4.3 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.zh-CN.vtt 4.3 kB
  • Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/index.html 4.3 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.en-US.vtt 4.3 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.pt-BR.vtt 4.3 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/index.html 4.3 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.en-US.vtt 4.3 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/index.html 4.3 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.en.vtt 4.3 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.pt-BR.vtt 4.3 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/maze.png 4.3 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.pt-BR.vtt 4.3 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/img/screen-shot-2018-05-22-at-12.27.22-pm.png 4.3 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.pt-BR.vtt 4.3 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/index.html 4.3 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/index.html 4.3 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.en.vtt 4.3 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt 4.3 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt 4.3 kB
  • Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.en-US.vtt 4.3 kB
  • Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.en.vtt 4.3 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.zh-CN.vtt 4.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.en.vtt 4.3 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.en.vtt 4.3 kB
  • Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.pt-BR.vtt 4.3 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/index.html 4.3 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/index.html 4.2 kB
  • Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.en.vtt 4.2 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/index.html 4.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.zh-CN.vtt 4.2 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/index.html 4.2 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.pt-BR.vtt 4.2 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.en-US.vtt 4.2 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.en.vtt 4.2 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.en.vtt 4.2 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt 4.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt 4.2 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.zh-CN.vtt 4.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.en.vtt 4.2 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.en.vtt 4.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt 4.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.zh-CN.vtt 4.2 kB
  • Part 04-Module 08-Lesson 01_Creating Customer Segments/index.html 4.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.en.vtt 4.2 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/index.html 4.2 kB
  • Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/index.html 4.2 kB
  • Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.en-US.vtt 4.2 kB
  • Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.en.vtt 4.2 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/index.html 4.2 kB
  • Part 07-Module 02-Lesson 01_Machine Learning Capstone Project/index.html 4.2 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/index.html 4.2 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.pt-BR.vtt 4.2 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.pt-BR.vtt 4.2 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.zh-CN.vtt 4.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.pt-BR.vtt 4.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.zh-CN.vtt 4.2 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.en-US.vtt 4.2 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.en-US.vtt 4.2 kB
  • Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.en.vtt 4.2 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.en.vtt 4.2 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.en.vtt 4.2 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.pt-BR.vtt 4.1 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/index.html 4.1 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.pt-BR.vtt 4.1 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.pt-BR.vtt 4.1 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.en-US.vtt 4.1 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.zh-CN.vtt 4.1 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.en.vtt 4.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.en.vtt 4.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt 4.1 kB
  • Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/index.html 4.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.en-US.vtt 4.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.en.vtt 4.1 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.pt-BR.vtt 4.1 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.en.vtt 4.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.en.vtt 4.1 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.pt-BR.vtt 4.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.pt-BR.vtt 4.1 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.zh-CN.vtt 4.1 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.pt-BR.vtt 4.1 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/index.html 4.1 kB
  • Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.ar.vtt 4.1 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.en.vtt 4.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.zh-CN.vtt 4.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt 4.0 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.en.vtt 4.0 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.ar.vtt 4.0 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.pt-BR.vtt 4.0 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.pt-BR.vtt 4.0 kB
  • Part 05-Module 01-Lesson 02_Cloud Computing/index.html 4.0 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.pt-BR.vtt 4.0 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.zh-CN.vtt 4.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.en.vtt 4.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.en.vtt 4.0 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.en.vtt 4.0 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.en.vtt 4.0 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.pt-BR.vtt 4.0 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.pt-BR.vtt 4.0 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.pt-BR.vtt 4.0 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.en-US.vtt 4.0 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.en.vtt 4.0 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.zh-CN.vtt 4.0 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.pt-BR.vtt 4.0 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.en.vtt 4.0 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.en.vtt 4.0 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/index.html 4.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.zh-CN.vtt 4.0 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.pt-BR.vtt 4.0 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.zh-CN.vtt 4.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.en.vtt 3.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.en.vtt 3.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.en.vtt 3.9 kB
  • Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.pt-BR.vtt 3.9 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.pt-BR.vtt 3.9 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.en.vtt 3.9 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.zh-CN.vtt 3.9 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.zh-CN.vtt 3.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.pt-BR.vtt 3.9 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.pt-BR.vtt 3.9 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.zh-CN.vtt 3.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.pt-BR.vtt 3.9 kB
  • Part 09-Module 01-Lesson 02_LinkedIn Review/index.html 3.9 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/m.gif 3.9 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.pt-BR.vtt 3.9 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.en.vtt 3.9 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.en.vtt 3.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.zh-CN.vtt 3.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.en.vtt 3.9 kB
  • Part 05-Module 01-Lesson 07_Deep Learning Project/index.html 3.9 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.pt-BR.vtt 3.9 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/index.html 3.9 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/index.html 3.9 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.zh-CN.vtt 3.9 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.pt-BR.vtt 3.9 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.en.vtt 3.9 kB
  • assets/css/fonts/KaTeX_Size3-Regular.woff2 3.9 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.en.vtt 3.9 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.pt-BR.vtt 3.9 kB
  • assets/css/styles.css 3.9 kB
  • Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.pt-BR.vtt 3.9 kB
  • Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.zh-CN.vtt 3.8 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.zh-CN.vtt 3.8 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.pt-BR.vtt 3.8 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.en.vtt 3.8 kB
  • Part 01-Module 01-Lesson 03_Introductory Practice Project/index.html 3.8 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.pt-BR.vtt 3.8 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.en.vtt 3.8 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.zh-CN.vtt 3.8 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/index.html 3.8 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.zh-CN.vtt 3.8 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.zh-CN.vtt 3.8 kB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.ar.vtt 3.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.pt-BR.vtt 3.8 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.pt-BR.vtt 3.8 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.pt-BR.vtt 3.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.zh-CN.vtt 3.8 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.pt-BR.vtt 3.8 kB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.ar.vtt 3.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt 3.8 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.zh-CN.vtt 3.8 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt 3.8 kB
  • Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.ar.vtt 3.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt 3.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.es-MX.vtt 3.8 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.es-MX.vtt 3.8 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.es-MX.vtt 3.8 kB
  • Part 11-Module 01-Lesson 02_Deep Learning/index.html 3.8 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.zh-CN.vtt 3.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.en.vtt 3.7 kB
  • Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.en.vtt 3.7 kB
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.zh-CN.vtt 3.7 kB
  • Part 04-Module 02-Lesson 02_Clustering Mini-Project/index.html 3.7 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.pt-BR.vtt 3.7 kB
  • Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/index.html 3.7 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/index.html 3.7 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.en-US.vtt 3.7 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.zh-CN.vtt 3.7 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt 3.7 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.en.vtt 3.7 kB
  • Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.zh-CN.vtt 3.7 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.pt-BR.vtt 3.7 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.zh-CN.vtt 3.7 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.en.vtt 3.7 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.zh-CN.vtt 3.7 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.zh-CN.vtt 3.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt 3.7 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.en.vtt 3.7 kB
  • Part 02-Module 04-Lesson 02_Model Evaluation and Validation Assessment/index.html 3.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.zh-CN.vtt 3.7 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.pt-BR.vtt 3.7 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.zh-CN.vtt 3.6 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.es-MX.vtt 3.6 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.pt-BR.vtt 3.6 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.pt-BR.vtt 3.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.en.vtt 3.6 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.en.vtt 3.6 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.en.vtt 3.6 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.zh-CN.vtt 3.6 kB
  • Part 03-Module 01-Lesson 07_Supervised Learning Assessment/index.html 3.6 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt 3.6 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.en-US.vtt 3.6 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.en.vtt 3.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.en.vtt 3.6 kB
  • Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.zh-CN.vtt 3.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.en.vtt 3.6 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.en-US.vtt 3.6 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.en-US.vtt 3.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.pt-BR.vtt 3.6 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.en.vtt 3.6 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.en.vtt 3.6 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.pt-BR.vtt 3.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt 3.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt 3.6 kB
  • Part 06-Module 03-Lesson 01_Reinforcement Learning Assessment/index.html 3.6 kB
  • Part 04-Module 07-Lesson 01_Unsupervised Learning Assessment/index.html 3.6 kB
  • Part 02-Module 04-Lesson 01_NumPy and pandas Assessment/index.html 3.6 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.zh-CN.vtt 3.6 kB
  • Part 05-Module 01-Lesson 06_Deep Learning Assessment/index.html 3.6 kB
  • Part 04-Module 05-Lesson 01_PCA Mini-Project/index.html 3.6 kB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/index.html 3.6 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.pt-BR.vtt 3.6 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.pt-BR.vtt 3.6 kB
  • Part 11-Module 01-Lesson 01_Software and Tools/index.html 3.5 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.en.vtt 3.5 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.en.vtt 3.5 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.en.vtt 3.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.pt-BR.vtt 3.5 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt 3.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt 3.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.en.vtt 3.5 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.pt-BR.vtt 3.5 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.en-US.vtt 3.5 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.en.vtt 3.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt 3.5 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.pt-BR.vtt 3.5 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.zh-CN.vtt 3.5 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt 3.5 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.pt-BR.vtt 3.5 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.zh-CN.vtt 3.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt 3.5 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.en.vtt 3.5 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.zh-CN.vtt 3.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.en.vtt 3.5 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.en.vtt 3.5 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.en.vtt 3.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.zh-CN.vtt 3.5 kB
  • Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.pt-BR.vtt 3.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.en.vtt 3.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.pt-BR.vtt 3.5 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.pt-BR.vtt 3.5 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.en.vtt 3.5 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.zh-CN.vtt 3.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.zh-CN.vtt 3.5 kB
  • Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.en-US.vtt 3.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.pt-BR.vtt 3.5 kB
  • Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.en.vtt 3.5 kB
  • Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.pt-BR.vtt 3.5 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt 3.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt 3.4 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.pt-BR.vtt 3.4 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.zh-CN.vtt 3.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.en.vtt 3.4 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.zh-CN.vtt 3.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.pt-BR.vtt 3.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt 3.4 kB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.ar.vtt 3.4 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt 3.4 kB
  • Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.en-US.vtt 3.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.zh-CN.vtt 3.4 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.en.vtt 3.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt 3.4 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.zh-CN.vtt 3.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/heaviside-step-function-2.gif 3.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.pt-BR.vtt 3.4 kB
  • Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.en-US.vtt 3.4 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.zh-CN.vtt 3.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.pt-BR.vtt 3.4 kB
  • Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.en.vtt 3.4 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.en-US.vtt 3.4 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.en.vtt 3.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.en.vtt 3.4 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.pt-BR.vtt 3.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.pt-BR.vtt 3.4 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt 3.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt 3.4 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.pt-BR.vtt 3.3 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt 3.3 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt 3.3 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt 3.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.zh-CN.vtt 3.3 kB
  • Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.en-US.vtt 3.3 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.en.vtt 3.3 kB
  • Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.en.vtt 3.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.pt-BR.vtt 3.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.zh-CN.vtt 3.3 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.en.vtt 3.3 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.zh-CN.vtt 3.3 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.pt-BR.vtt 3.3 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.es-MX.vtt 3.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.en.vtt 3.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.en.vtt 3.3 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/mse.png 3.3 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.es-MX.vtt 3.3 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.en-US.vtt 3.3 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.en.vtt 3.3 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.pt-BR.vtt 3.3 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.pt-BR.vtt 3.3 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.en.vtt 3.3 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.en.vtt 3.3 kB
  • Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.en.vtt 3.3 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.en-US.vtt 3.2 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.en-US.vtt 3.2 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.en.vtt 3.2 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.en.vtt 3.2 kB
  • Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.en-US.vtt 3.2 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.pt-BR.vtt 3.2 kB
  • Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.en.vtt 3.2 kB
  • Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.pt-BR.vtt 3.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.pt-BR.vtt 3.2 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.ar.vtt 3.2 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt 3.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt 3.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.pt-BR.vtt 3.2 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.pt-BR.vtt 3.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.zh-CN.vtt 3.2 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.zh-CN.vtt 3.2 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.en.vtt 3.2 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.pt-BR.vtt 3.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.pt-BR.vtt 3.2 kB
  • Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.pt-BR.vtt 3.2 kB
  • Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.pt-BR.vtt 3.2 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.zh-CN.vtt 3.2 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.en.vtt 3.2 kB
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt 3.2 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.pt-BR.vtt 3.2 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.pt-BR.vtt 3.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.pt-BR.vtt 3.2 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.es-MX.vtt 3.2 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.en.vtt 3.1 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.zh-CN.vtt 3.1 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.en.vtt 3.1 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.pt-BR.vtt 3.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.en.vtt 3.1 kB
  • Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.ar.vtt 3.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.en.vtt 3.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.zh-CN.vtt 3.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.zh-CN.vtt 3.1 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.en.vtt 3.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.en.vtt 3.1 kB
  • Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.zh-CN.vtt 3.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.pt-BR.vtt 3.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.zh-CN.vtt 3.1 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.zh-CN.vtt 3.1 kB
  • Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.zh-CN.vtt 3.1 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.pt-BR.vtt 3.1 kB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.pt-BR.vtt 3.1 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.pt-BR.vtt 3.1 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.ar.vtt 3.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.en-US.vtt 3.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.en.vtt 3.1 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.pt-BR.vtt 3.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt 3.1 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.pt-BR.vtt 3.1 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.zh-CN.vtt 3.1 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.zh-CN.vtt 3.1 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.en.vtt 3.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.en.vtt 3.1 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.en-US.vtt 3.1 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.zh-CN.vtt 3.1 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.pt-BR.vtt 3.1 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.zh-CN.vtt 3.1 kB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.ar.vtt 3.1 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.zh-CN.vtt 3.1 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.en.vtt 3.1 kB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.en.vtt 3.1 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.en.vtt 3.0 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.pt-BR.vtt 3.0 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.en.vtt 3.0 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.pt-BR.vtt 3.0 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.pt-BR.vtt 3.0 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.pt-BR.vtt 3.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.en.vtt 3.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt 3.0 kB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.pt-BR.vtt 3.0 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.en.vtt 3.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.en.vtt 3.0 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.en-US.vtt 3.0 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.en.vtt 3.0 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.en.vtt 3.0 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-error.gif 3.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.ar.vtt 3.0 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.en.vtt 3.0 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.pt-BR.vtt 3.0 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.zh-CN.vtt 3.0 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.zh-CN.vtt 3.0 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.zh-CN.vtt 3.0 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.en.vtt 3.0 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.en.vtt 3.0 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.zh-CN.vtt 3.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.zh-CN.vtt 3.0 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.zh-CN.vtt 3.0 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.zh-CN.vtt 3.0 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.zh-CN.vtt 3.0 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.en.vtt 3.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt 3.0 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.zh-CN.vtt 2.9 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.zh-CN.vtt 2.9 kB
  • Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.pt-BR.vtt 2.9 kB
  • Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.zh-CN.vtt 2.9 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.en.vtt 2.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.pt-BR.vtt 2.9 kB
  • Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.en-US.vtt 2.9 kB
  • Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.en.vtt 2.9 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.zh-CN.vtt 2.9 kB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.en.vtt 2.9 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.en.vtt 2.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.pt-BR.vtt 2.9 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.zh-CN.vtt 2.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/weight-label-reference.gif 2.9 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.pt-BR.vtt 2.9 kB
  • Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.zh-CN.vtt 2.9 kB
  • Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.pt-BR.vtt 2.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.en.vtt 2.9 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt 2.9 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt 2.9 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt 2.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.en.vtt 2.9 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.ar.vtt 2.9 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.pt-BR.vtt 2.9 kB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.pt-BR.vtt 2.9 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.zh-CN.vtt 2.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.zh-CN.vtt 2.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.en.vtt 2.9 kB
  • Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.zh-CN.vtt 2.9 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.pt-BR.vtt 2.9 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.en.vtt 2.9 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.zh-CN.vtt 2.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hidden-errors.gif 2.9 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.zh-CN.vtt 2.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.pt-BR.vtt 2.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.pt-BR.vtt 2.9 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.zh-CN.vtt 2.9 kB
  • Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.en.vtt 2.9 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.pt-BR.vtt 2.9 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.pt-BR.vtt 2.8 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.pt-BR.vtt 2.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.en.vtt 2.8 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.pt-BR.vtt 2.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt 2.8 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.pt-BR.vtt 2.8 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.en-US.vtt 2.8 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.en.vtt 2.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.en.vtt 2.8 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.en.vtt 2.8 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.en.vtt 2.8 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.zh-CN.vtt 2.8 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.es-MX.vtt 2.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.8 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.8 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt 2.8 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt 2.8 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.zh-CN.vtt 2.8 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.en.vtt 2.8 kB
  • Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.zh-CN.vtt 2.8 kB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.ar.vtt 2.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.zh-CN.vtt 2.8 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.zh-CN.vtt 2.8 kB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.en.vtt 2.8 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt 2.8 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.es-MX.vtt 2.8 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.en.vtt 2.8 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.en.vtt 2.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.en.vtt 2.8 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.pt-BR.vtt 2.8 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.pt-BR.vtt 2.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.pt-BR.vtt 2.8 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.en.vtt 2.8 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.en.vtt 2.7 kB
  • Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.en-US.vtt 2.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.en.vtt 2.7 kB
  • Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.en.vtt 2.7 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.pt-BR.vtt 2.7 kB
  • Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.zh-CN.vtt 2.7 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.pt-BR.vtt 2.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.pt-BR.vtt 2.7 kB
  • Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.pt-BR.vtt 2.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.pt-BR.vtt 2.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.en.vtt 2.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt 2.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt 2.7 kB
  • Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.pt-BR.vtt 2.7 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.pt-BR.vtt 2.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.en.vtt 2.7 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.en.vtt 2.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.zh-CN.vtt 2.7 kB
  • Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.en-US.vtt 2.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.en.vtt 2.7 kB
  • Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.en.vtt 2.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.en.vtt 2.7 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.pt-BR.vtt 2.7 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.en.vtt 2.7 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.en.vtt 2.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.en.vtt 2.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.en.vtt 2.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt 2.7 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.en-US.vtt 2.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.pt-BR.vtt 2.7 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.pt-BR.vtt 2.7 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.es-MX.vtt 2.7 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.zh-CN.vtt 2.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.pt-BR.vtt 2.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.ar.vtt 2.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.en.vtt 2.7 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.es-MX.vtt 2.7 kB
  • Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.en.vtt 2.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.zh-CN.vtt 2.6 kB
  • Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.en-US.vtt 2.6 kB
  • Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.en.vtt 2.6 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.6 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.6 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt 2.6 kB
  • Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.zh-CN.vtt 2.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.zh-CN.vtt 2.6 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.pt-BR.vtt 2.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.pt-BR.vtt 2.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.ar.vtt 2.6 kB
  • Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.pt-BR.vtt 2.6 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.en.vtt 2.6 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.zh-CN.vtt 2.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.pt-BR.vtt 2.6 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.zh-CN.vtt 2.6 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt 2.6 kB
  • Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en-US.vtt 2.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt 2.6 kB
  • Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en.vtt 2.6 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.6 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.6 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt 2.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.zh-CN.vtt 2.6 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.6 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.6 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt 2.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.zh-CN.vtt 2.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt 2.6 kB
  • Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en-US.vtt 2.6 kB
  • Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en.vtt 2.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en-US.vtt 2.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.pt-BR.vtt 2.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.pt-BR.vtt 2.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en.vtt 2.6 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.pt-BR.vtt 2.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.pt-BR.vtt 2.6 kB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.zh-CN.vtt 2.6 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.pt-BR.vtt 2.6 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.zh-CN.vtt 2.6 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.en.vtt 2.6 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.zh-CN.vtt 2.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.pt-BR.vtt 2.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en-US.vtt 2.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.en.vtt 2.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en.vtt 2.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.zh-CN.vtt 2.6 kB
  • Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.pt-BR.vtt 2.6 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.en.vtt 2.6 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.pt-BR.vtt 2.6 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.zh-CN.vtt 2.6 kB
  • Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.pt-BR.vtt 2.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.en.vtt 2.5 kB
  • Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en-US.vtt 2.5 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.pt-BR.vtt 2.5 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.en.vtt 2.5 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.pt-BR.vtt 2.5 kB
  • Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en.vtt 2.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.en.vtt 2.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.pt-BR.vtt 2.5 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt 2.5 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.zh-CN.vtt 2.5 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.en.vtt 2.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.pt-BR.vtt 2.5 kB
  • Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en-US.vtt 2.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.zh-CN.vtt 2.5 kB
  • Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en.vtt 2.5 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.en.vtt 2.5 kB
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.zh-CN.vtt 2.5 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.zh-CN.vtt 2.5 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.es-MX.vtt 2.5 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.en.vtt 2.5 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.pt-BR.vtt 2.5 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.pt-BR.vtt 2.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.pt-BR.vtt 2.5 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.pt-BR.vtt 2.5 kB
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.zh-CN.vtt 2.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.en-US.vtt 2.5 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.pt-BR.vtt 2.5 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt 2.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt 2.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.pt-BR.vtt 2.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.pt-BR.vtt 2.5 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.pt-BR.vtt 2.5 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.en.vtt 2.5 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt 2.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.en.vtt 2.5 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.pt-BR.vtt 2.5 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.5 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.5 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt 2.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.pt-BR.vtt 2.5 kB
  • Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.pt-BR.vtt 2.5 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.pt-BR.vtt 2.5 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.pt-BR.vtt 2.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.en.vtt 2.4 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt 2.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt 2.4 kB
  • Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.en-US.vtt 2.4 kB
  • Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.pt-BR.vtt 2.4 kB
  • Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.en.vtt 2.4 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.pt-BR.vtt 2.4 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.zh-CN.vtt 2.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.zh-CN.vtt 2.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.pt-BR.vtt 2.4 kB
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.zh-CN.vtt 2.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.pt-BR.vtt 2.4 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.en.vtt 2.4 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.4 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.4 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt 2.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.en.vtt 2.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.zh-CN.vtt 2.4 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.en-US.vtt 2.4 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt 2.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt 2.4 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.en.vtt 2.4 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.zh-CN.vtt 2.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt 2.4 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.en.vtt 2.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.pt-BR.vtt 2.4 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.zh-CN.vtt 2.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.zh-CN.vtt 2.4 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.en.vtt 2.4 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt 2.4 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt 2.4 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt 2.4 kB
  • Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.pt-BR.vtt 2.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.zh-CN.vtt 2.4 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.pt-BR.vtt 2.4 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.en.vtt 2.4 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.4 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.4 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt 2.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.pt-BR.vtt 2.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.en.vtt 2.4 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.zh-CN.vtt 2.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.zh-CN.vtt 2.4 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.ar.vtt 2.4 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.ar.vtt 2.4 kB
  • Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en-US.vtt 2.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt 2.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.en.vtt 2.4 kB
  • Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.zh-CN.vtt 2.4 kB
  • Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en.vtt 2.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.en.vtt 2.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt 2.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.pt-BR.vtt 2.4 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en-US.vtt 2.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.en.vtt 2.3 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en.vtt 2.3 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.en-US.vtt 2.3 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.en.vtt 2.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.en.vtt 2.3 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt 2.3 kB
  • Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.pt-BR.vtt 2.3 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.en.vtt 2.3 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.en.vtt 2.3 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.pt-BR.vtt 2.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.pt-BR.vtt 2.3 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.zh-CN.vtt 2.3 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.ar.vtt 2.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.ar.vtt 2.3 kB
  • Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.en-US.vtt 2.3 kB
  • Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.en.vtt 2.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.zh-CN.vtt 2.3 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.pt-BR.vtt 2.3 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/codecogseqn-2.png 2.3 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt 2.3 kB
  • Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.zh-CN.vtt 2.3 kB
  • Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.pt-BR.vtt 2.3 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.es-MX.vtt 2.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt 2.3 kB
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.zh-CN.vtt 2.3 kB
  • Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.pt-BR.vtt 2.3 kB
  • Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.zh-CN.vtt 2.3 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.pt-BR.vtt 2.3 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt 2.3 kB
  • Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.zh-CN.vtt 2.3 kB
  • Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.en.vtt 2.3 kB
  • Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.en-US.vtt 2.3 kB
  • Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.zh-CN.vtt 2.3 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.en-US.vtt 2.3 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.zh-CN.vtt 2.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.pt-BR.vtt 2.3 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.pt-BR.vtt 2.3 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.en.vtt 2.3 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.zh-CN.vtt 2.3 kB
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.zh-CN.vtt 2.3 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.en.vtt 2.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.zh-CN.vtt 2.3 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.en.vtt 2.3 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.en.vtt 2.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.zh-CN.vtt 2.3 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt 2.3 kB
  • Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.en.vtt 2.3 kB
  • Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.zh-CN.vtt 2.3 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.pt-BR.vtt 2.3 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-general.gif 2.3 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.en.vtt 2.2 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.ar.vtt 2.2 kB
  • Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.zh-CN.vtt 2.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.pt-BR.vtt 2.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.zh-CN.vtt 2.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.en.vtt 2.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt 2.2 kB
  • Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.zh-CN.vtt 2.2 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.2 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.2 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt 2.2 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.pt-BR.vtt 2.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.ar.vtt 2.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.en.vtt 2.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.en.vtt 2.2 kB
  • Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.pt-BR.vtt 2.2 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.zh-CN.vtt 2.2 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.zh-CN.vtt 2.2 kB
  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.ar.vtt 2.2 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.pt-BR.vtt 2.2 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.en.vtt 2.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt 2.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt 2.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.zh-CN.vtt 2.2 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.zh-CN.vtt 2.2 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.zh-CN.vtt 2.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.pt-BR.vtt 2.2 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.pt-BR.vtt 2.2 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.zh-CN.vtt 2.2 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.zh-CN.vtt 2.2 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.zh-CN.vtt 2.2 kB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.ar.vtt 2.2 kB
  • Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.zh-CN.vtt 2.2 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt 2.2 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt 2.2 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt 2.2 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt 2.2 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt 2.2 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt 2.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.zh-CN.vtt 2.1 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.zh-CN.vtt 2.1 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.zh-CN.vtt 2.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-49.gif 2.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/img/sigmoid-derivative.gif 2.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.en-US.vtt 2.1 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.zh-CN.vtt 2.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.en.vtt 2.1 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt 2.1 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt 2.1 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt 2.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.en.vtt 2.1 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.es-MX.vtt 2.1 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.en.vtt 2.1 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.en.vtt 2.1 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.zh-CN.vtt 2.1 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.en.vtt 2.1 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/codecogseqn-61.gif 2.1 kB
  • Part 02-Module 03-Lesson 01_Model Selection/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.pt-BR.vtt 2.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt 2.1 kB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.en.vtt 2.1 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.pt-BR.vtt 2.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.en.vtt 2.1 kB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.ar.vtt 2.1 kB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.en.vtt 2.1 kB
  • Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.zh-CN.vtt 2.1 kB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.ar.vtt 2.1 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.en.vtt 2.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.zh-CN.vtt 2.1 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.en-US.vtt 2.1 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.zh-CN.vtt 2.1 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.pt-BR.vtt 2.1 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.en.vtt 2.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.zh-CN.vtt 2.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.pt-BR.vtt 2.1 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.pt-BR.vtt 2.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.en.vtt 2.1 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.zh-CN.vtt 2.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.pt-BR.vtt 2.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt 2.1 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.en.vtt 2.1 kB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.pt-BR.vtt 2.1 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.pt-BR.vtt 2.1 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt 2.1 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.zh-CN.vtt 2.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.pt-BR.vtt 2.1 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/f1.gif 2.1 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.zh-CN.vtt 2.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.zh-CN.vtt 2.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.pt-BR.vtt 2.1 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.pt-BR.vtt 2.1 kB
  • Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.pt-BR.vtt 2.1 kB
  • Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.pt-BR.vtt 2.0 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.zh-CN.vtt 2.0 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.es-MX.vtt 2.0 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.zh-CN.vtt 2.0 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.en-US.vtt 2.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.pt-BR.vtt 2.0 kB
  • Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.en-US.vtt 2.0 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.en.vtt 2.0 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.en.vtt 2.0 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.pt-BR.vtt 2.0 kB
  • Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.en.vtt 2.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.en.vtt 2.0 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt 2.0 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt 2.0 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt 2.0 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm-BR.vtt 2.0 kB
  • Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.pt-BR.vtt 2.0 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.zh-CN.vtt 2.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.zh-CN.vtt 2.0 kB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.pt-BR.vtt 2.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.en.vtt 2.0 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.pt-BR.vtt 2.0 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.zh-CN.vtt 2.0 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.zh-CN.vtt 2.0 kB
  • Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.zh-CN.vtt 2.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt 2.0 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt 2.0 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt 2.0 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt 2.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.zh-CN.vtt 2.0 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.ar.vtt 2.0 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.pt-BR.vtt 2.0 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.pt-BR.vtt 2.0 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.en.vtt 2.0 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt 2.0 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.en.vtt 2.0 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.zh-CN.vtt 2.0 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.ar.vtt 2.0 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.en.vtt 2.0 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.zh-CN.vtt 2.0 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.en.vtt 2.0 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.zh-CN.vtt 2.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.en-US.vtt 2.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.en.vtt 2.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.zh-CN.vtt 1.9 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.es-MX.vtt 1.9 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.zh-CN.vtt 1.9 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.pt-BR.vtt 1.9 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.pt-BR.vtt 1.9 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.en.vtt 1.9 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.zh-CN.vtt 1.9 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/f2.gif 1.9 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.pt-BR.vtt 1.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.pt-BR.vtt 1.9 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.zh-CN.vtt 1.9 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.pt-BR.vtt 1.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.pt-BR.vtt 1.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.pt-BR.vtt 1.9 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.pt-BR.vtt 1.9 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.zh-CN.vtt 1.9 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.pt-BR.vtt 1.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.zh-CN.vtt 1.9 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.en.vtt 1.9 kB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.ar.vtt 1.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.en.vtt 1.9 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt 1.9 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt 1.9 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt 1.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.zh-CN.vtt 1.9 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.pt-BR.vtt 1.9 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.en.vtt 1.9 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.zh-CN.vtt 1.9 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.ja-JP.vtt 1.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.en-US.vtt 1.9 kB
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.zh-CN.vtt 1.9 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.es-MX.vtt 1.9 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.en.vtt 1.9 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.en.vtt 1.9 kB
  • Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.zh-CN.vtt 1.9 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.en.vtt 1.9 kB
  • Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.en-US.vtt 1.9 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.zh-CN.vtt 1.9 kB
  • Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.en.vtt 1.9 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt 1.9 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.zh-CN.vtt 1.9 kB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.ar.vtt 1.9 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt 1.8 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt 1.8 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt 1.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.en.vtt 1.8 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.en.vtt 1.8 kB
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.zh-CN.vtt 1.8 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.pt-BR.vtt 1.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.en.vtt 1.8 kB
  • Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.pt-BR.vtt 1.8 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.zh-CN.vtt 1.8 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.pt-BR.vtt 1.8 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.zh-CN.vtt 1.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.pt-BR.vtt 1.8 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt 1.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.en.vtt 1.8 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.zh-CN.vtt 1.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt 1.8 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.zh-CN.vtt 1.8 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm.en.vtt 1.8 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt 1.8 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.en.vtt 1.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt 1.8 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt 1.8 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt 1.8 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.zh-CN.vtt 1.8 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hidden-layer-weights.gif 1.8 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.ar.vtt 1.8 kB
  • Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.zh-CN.vtt 1.8 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.zh-CN.vtt 1.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.pt-BR.vtt 1.8 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.pt-BR.vtt 1.8 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.zh-CN.vtt 1.8 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.en.vtt 1.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.pt-BR.vtt 1.8 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.pt-BR.vtt 1.8 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.ar.vtt 1.8 kB
  • Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.pt-BR.vtt 1.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt 1.8 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.pt-BR.vtt 1.8 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.en.vtt 1.8 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.en.vtt 1.8 kB
  • Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.pt-BR.vtt 1.8 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.pt-BR.vtt 1.8 kB
  • Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt 1.7 kB
  • Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt 1.7 kB
  • Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt 1.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.pt-BR.vtt 1.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.pt-BR.vtt 1.7 kB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.ar.vtt 1.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.pt-BR.vtt 1.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.en.vtt 1.7 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.en-US.vtt 1.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.pt-BR.vtt 1.7 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.en.vtt 1.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt 1.7 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.en.vtt 1.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.zh-CN.vtt 1.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.pt-BR.vtt 1.7 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt 1.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-weight-update.gif 1.7 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.pt-BR.vtt 1.7 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.zh-CN.vtt 1.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.pt-BR.vtt 1.7 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.zh-CN.vtt 1.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.zh-CN.vtt 1.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.en.vtt 1.7 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.zh-CN.vtt 1.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.en.vtt 1.7 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.en.vtt 1.7 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt 1.7 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.en.vtt 1.7 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.zh-CN.vtt 1.7 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.zh-CN.vtt 1.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.zh-CN.vtt 1.7 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.zh-CN.vtt 1.7 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.zh-CN.vtt 1.7 kB
  • Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.en.vtt 1.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt 1.7 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.zh-CN.vtt 1.7 kB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.en.vtt 1.7 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.zh-CN.vtt 1.7 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.ar.vtt 1.7 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.zh-CN.vtt 1.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt 1.7 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.zh-CN.vtt 1.7 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.pt-BR.vtt 1.7 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt 1.7 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.en.vtt 1.7 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.zh-CN.vtt 1.7 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.en.vtt 1.6 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.en.vtt 1.6 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.pt-BR.vtt 1.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.pt-BR.vtt 1.6 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.pt-BR.vtt 1.6 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/f6.gif 1.6 kB
  • Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.zh-CN.vtt 1.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.ar.vtt 1.6 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.pt-BR.vtt 1.6 kB
  • Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.zh-CN.vtt 1.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.pt-BR.vtt 1.6 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.en.vtt 1.6 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.pt-BR.vtt 1.6 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.en.vtt 1.6 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.en.vtt 1.6 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.en-US.vtt 1.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.en.vtt 1.6 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.en.vtt 1.6 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.en.vtt 1.6 kB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.ar.vtt 1.6 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt 1.6 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.pt-BR.vtt 1.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.en.vtt 1.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.pt-BR.vtt 1.6 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt 1.6 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.en.vtt 1.6 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.zh-CN.vtt 1.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.en.vtt 1.6 kB
  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.en.vtt 1.6 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.en-US.vtt 1.6 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.zh-CN.vtt 1.6 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.pt-BR.vtt 1.6 kB
  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.pt-BR.vtt 1.6 kB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.pt-BR.vtt 1.6 kB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.pt-BR.vtt 1.6 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.en.vtt 1.6 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.zh-CN.vtt 1.6 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.pt-BR.vtt 1.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.en-US.vtt 1.6 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.zh-CN.vtt 1.6 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en-US.vtt 1.6 kB
  • Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.zh-CN.vtt 1.6 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.en.vtt 1.6 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.pt-BR.vtt 1.6 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.zh-CN.vtt 1.6 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.pt-BR.vtt 1.6 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en.vtt 1.6 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.pt-BR.vtt 1.6 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.es-MX.vtt 1.6 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.en.vtt 1.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt 1.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt 1.5 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.pt-BR.vtt 1.5 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en-US.vtt 1.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.en.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en.vtt 1.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.pt-BR.vtt 1.5 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.pt-BR.vtt 1.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.pt-BR.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.pt-BR.vtt 1.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.zh-CN.vtt 1.5 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.zh-CN.vtt 1.5 kB
  • Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.es-MX.vtt 1.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.en.vtt 1.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.en.vtt 1.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.zh-CN.vtt 1.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.pt-BR.vtt 1.5 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt 1.5 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.en.vtt 1.5 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.pt-BR.vtt 1.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.pt-BR.vtt 1.5 kB
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.zh-CN.vtt 1.5 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.en.vtt 1.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt 1.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.en.vtt 1.5 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.pt-BR.vtt 1.5 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.pt-BR.vtt 1.5 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.zh-CN.vtt 1.5 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.pt-BR.vtt 1.5 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.en.vtt 1.5 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.en-US.vtt 1.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.pt-BR.vtt 1.5 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.en.vtt 1.5 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.en.vtt 1.5 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.en.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.zh-CN.vtt 1.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt 1.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm-CN.vtt 1.5 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.es-MX.vtt 1.5 kB
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.zh-CN.vtt 1.5 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.en.vtt 1.5 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.zh-CN.vtt 1.5 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.zh-CN.vtt 1.5 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt 1.5 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.pt-BR.vtt 1.5 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.en.vtt 1.5 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.en.vtt 1.5 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.zh-CN.vtt 1.5 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.en.vtt 1.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.zh-CN.vtt 1.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/y.gif 1.4 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.zh-CN.vtt 1.4 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.en.vtt 1.4 kB
  • Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.pt-BR.vtt 1.4 kB
  • Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.pt-BR.vtt 1.4 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.en.vtt 1.4 kB
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt 1.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt 1.4 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.pt-BR.vtt 1.4 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.en.vtt 1.4 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.zh-CN.vtt 1.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.zh-CN.vtt 1.4 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.zh-CN.vtt 1.4 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.pt-BR.vtt 1.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.en.vtt 1.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.pt-BR.vtt 1.4 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt 1.4 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.zh-CN.vtt 1.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.en.vtt 1.4 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.en.vtt 1.4 kB
  • Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.zh-CN.vtt 1.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.pt-BR.vtt 1.4 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.pt-BR.vtt 1.4 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.pt-BR.vtt 1.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.ar.vtt 1.4 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.zh-CN.vtt 1.4 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.zh-CN.vtt 1.4 kB
  • [FTU Forum].url 1.4 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt 1.4 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.pt-BR.vtt 1.4 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.en.vtt 1.4 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.zh-CN.vtt 1.4 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.en.vtt 1.4 kB
  • Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.en.vtt 1.4 kB
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.ar.vtt 1.4 kB
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.zh-CN.vtt 1.4 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.ar.vtt 1.3 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.zh-CN.vtt 1.3 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.en.vtt 1.3 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en-US.vtt 1.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.ar.vtt 1.3 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en.vtt 1.3 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/codecogseqn-62.gif 1.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt 1.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.zh-CN.vtt 1.3 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.pt-BR.vtt 1.3 kB
  • Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.zh-CN.vtt 1.3 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.pt-BR.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.pt-BR.vtt 1.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.en.vtt 1.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.zh-CN.vtt 1.3 kB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.en.vtt 1.3 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.en.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.zh-CN.vtt 1.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.pt-BR.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.pt-BR.vtt 1.3 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.zh-CN.vtt 1.3 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.zh-CN.vtt 1.3 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.pt-BR.vtt 1.3 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.en.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.en.vtt 1.3 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt 1.3 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.zh-CN.vtt 1.3 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.en.vtt 1.3 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.en-US.vtt 1.3 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.en.vtt 1.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt 1.3 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.en.vtt 1.3 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.zh-CN.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.ar.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.pt-BR.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.zh-CN.vtt 1.3 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.zh-CN.vtt 1.3 kB
  • Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.pt-BR.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.en.vtt 1.3 kB
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.zh-CN.vtt 1.3 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt 1.3 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.en.vtt 1.3 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw.en-US.vtt 1.3 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/linear-equation.gif 1.3 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.pt-BR.vtt 1.3 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.en.vtt 1.3 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.en.vtt 1.3 kB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.en.vtt 1.3 kB
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.ar.vtt 1.2 kB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.pt-BR.vtt 1.2 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.pt-BR.vtt 1.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.pt-BR.vtt 1.2 kB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.es-MX.vtt 1.2 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.zh-CN.vtt 1.2 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.zh-CN.vtt 1.2 kB
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.zh-CN.vtt 1.2 kB
  • Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.en-US.vtt 1.2 kB
  • Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.en.vtt 1.2 kB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.en.vtt 1.2 kB
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.en.vtt 1.2 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.en.vtt 1.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.zh-CN.vtt 1.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.pt-BR.vtt 1.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.en.vtt 1.2 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.en-US.vtt 1.2 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.en.vtt 1.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.pt-BR.vtt 1.2 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.pt-BR.vtt 1.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/e.gif 1.2 kB
  • Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.zh-CN.vtt 1.2 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.pt-BR.vtt 1.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.en.vtt 1.2 kB
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.ar.vtt 1.2 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt 1.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.pt-BR.vtt 1.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.zh-CN.vtt 1.2 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.pt-BR.vtt 1.2 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.en.vtt 1.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.en.vtt 1.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.en.vtt 1.2 kB
  • Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.zh-CN.vtt 1.2 kB
  • Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.pt-BR.vtt 1.2 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.en-US.vtt 1.2 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.en.vtt 1.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt 1.2 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.zh-CN.vtt 1.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.en.vtt 1.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt 1.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.en.vtt 1.2 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.en.vtt 1.2 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.zh-CN.vtt 1.2 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.zh-CN.vtt 1.2 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.pt-BR.vtt 1.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.en.vtt 1.2 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.en.vtt 1.2 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.pt-BR.vtt 1.2 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/f4.gif 1.2 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.pt-BR.vtt 1.2 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.zh-CN.vtt 1.2 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.zh-CN.vtt 1.2 kB
  • Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.zh-CN.vtt 1.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt 1.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.en.vtt 1.1 kB
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.zh-CN.vtt 1.1 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.en.vtt 1.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.en.vtt 1.1 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.ar.vtt 1.1 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.zh-CN.vtt 1.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.en-US.vtt 1.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw.zh-CN.vtt 1.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.pt-BR.vtt 1.1 kB
  • Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.zh-CN.vtt 1.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.zh-CN.vtt 1.1 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.pt-BR.vtt 1.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.pt-BR.vtt 1.1 kB
  • Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.zh-CN.vtt 1.1 kB
  • Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.pt-BR.vtt 1.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.pt-BR.vtt 1.1 kB
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.zh-CN.vtt 1.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.pt-BR.vtt 1.1 kB
  • Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.zh-CN.vtt 1.1 kB
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.pt-BR.vtt 1.1 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.en.vtt 1.1 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.pt-BR.vtt 1.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.en-US.vtt 1.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.pt-BR.vtt 1.1 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.en.vtt 1.1 kB
  • Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.zh-CN.vtt 1.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.en-US.vtt 1.1 kB
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.zh-CN.vtt 1.1 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.en.vtt 1.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.en.vtt 1.1 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.pt-BR.vtt 1.1 kB
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.en.vtt 1.1 kB
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.ar.vtt 1.1 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.pt-BR.vtt 1.1 kB
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw.pt-BR.vtt 1.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.en.vtt 1.1 kB
  • Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.en.vtt 1.1 kB
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.ar.vtt 1.1 kB
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.zh-CN.vtt 1.1 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/img/gif-1.gif 1.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.pt-BR.vtt 1.1 kB
  • Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.pt-BR.vtt 1.1 kB
  • Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.en-US.vtt 1.1 kB
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.en.vtt 1.1 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.en.vtt 1.0 kB
  • Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.en.vtt 1.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.pt-BR.vtt 1.0 kB
  • Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.zh-CN.vtt 1.0 kB
  • Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.zh-CN.vtt 1.0 kB
  • Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.es-MX.vtt 1.0 kB
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.pt-BR.vtt 1.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.zh-CN.vtt 1.0 kB
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.pt-BR.vtt 1.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.pt-BR.vtt 1.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.zh-CN.vtt 1.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.zh-CN.vtt 1.0 kB
  • Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.zh-CN.vtt 1.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.en.vtt 1.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.en.vtt 1.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.en.vtt 1.0 kB
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.en.vtt 1.0 kB
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.zh-CN.vtt 1.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.pt-BR.vtt 1.0 kB
  • Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.en.vtt 1.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt 1.0 kB
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.pt-BR.vtt 1.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt 1.0 kB
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt 1.0 kB
  • Part 05-Module 01-Lesson 01_Neural Networks/08. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt 1.0 kB
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.zh-CN.vtt 1.0 kB
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.ar.vtt 1.0 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.zh-CN.vtt 1.0 kB
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.ar.vtt 1.0 kB
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.ar.vtt 1.0 kB
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.pt-BR.vtt 1.0 kB
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.zh-CN.vtt 1.0 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.en-US.vtt 1.0 kB
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.zh-CN.vtt 1.0 kB
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.en.vtt 1.0 kB
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.ar.vtt 999 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.zh-CN.vtt 996 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.zh-CN.vtt 995 Bytes
  • Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.en.vtt 994 Bytes
  • Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.pt-BR.vtt 993 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.en.vtt 991 Bytes
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.en.vtt 989 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.en.vtt 984 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.en.vtt 983 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.pt-BR.vtt 977 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.pt-BR.vtt 977 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.ar.vtt 976 Bytes
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.pt-BR.vtt 975 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.pt-BR.vtt 970 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.zh-CN.vtt 969 Bytes
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.pt-BR.vtt 966 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.zh-CN.vtt 965 Bytes
  • Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.zh-CN.vtt 965 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.pt-BR.vtt 965 Bytes
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.en-US.vtt 960 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.pt-BR.vtt 959 Bytes
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.en.vtt 957 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.zh-CN.vtt 956 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/14. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.pt-BR.vtt 956 Bytes
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.pt-BR.vtt 955 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.pt-BR.vtt 954 Bytes
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.pt-BR.vtt 950 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt 947 Bytes
  • Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.pt-BR.vtt 945 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.zh-CN.vtt 944 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.en.vtt 943 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.en.vtt 943 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.en.vtt 939 Bytes
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.ar.vtt 938 Bytes
  • Part 04-Module 06-Lesson 01_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.en.vtt 938 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.pt-BR.vtt 937 Bytes
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.en.vtt 937 Bytes
  • Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.zh-CN.vtt 930 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.pt-BR.vtt 928 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.pt-BR.vtt 928 Bytes
  • Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.zh-CN.vtt 927 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.zh-CN.vtt 924 Bytes
  • Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.zh-CN.vtt 922 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.zh-CN.vtt 920 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-58.gif 919 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.en.vtt 918 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.zh-CN.vtt 916 Bytes
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.en.vtt 910 Bytes
  • Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.en-US.vtt 900 Bytes
  • Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.zh-CN.vtt 900 Bytes
  • Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.en.vtt 897 Bytes
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.en.vtt 896 Bytes
  • Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.pt-BR.vtt 895 Bytes
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.pt-BR.vtt 895 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.pt-BR.vtt 893 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.pt-BR.vtt 891 Bytes
  • Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.zh-CN.vtt 891 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.pt-BR.vtt 889 Bytes
  • Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.zh-CN.vtt 883 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.ar.vtt 882 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.pt-BR.vtt 880 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.en.vtt 879 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.zh-CN.vtt 879 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.pt-BR.vtt 874 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.en.vtt 874 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.en.vtt 867 Bytes
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.pt-BR.vtt 866 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.ar.vtt 865 Bytes
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.pt-BR.vtt 862 Bytes
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.zh-CN.vtt 862 Bytes
  • Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.en.vtt 857 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.pt-BR.vtt 857 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.pt-BR.vtt 856 Bytes
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.en.vtt 856 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.en.vtt 855 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.pt-BR.vtt 853 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.en.vtt 853 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.en.vtt 850 Bytes
  • Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.zh-CN.vtt 849 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt 845 Bytes
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.en.vtt 842 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.ar.vtt 842 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.ar.vtt 841 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.zh-CN.vtt 840 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.ar.vtt 836 Bytes
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.es-MX.vtt 832 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt 831 Bytes
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.en.vtt 830 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.zh-CN.vtt 829 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.en.vtt 828 Bytes
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.pt-BR.vtt 826 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.en.vtt 824 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt 823 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.zh-CN.vtt 822 Bytes
  • Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.zh-CN.vtt 822 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.en.vtt 820 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.ar.vtt 820 Bytes
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.pt-BR.vtt 817 Bytes
  • Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.zh-CN.vtt 814 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt 813 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.zh-CN.vtt 812 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt 812 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.zh-CN.vtt 810 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.zh-CN.vtt 810 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.zh-CN.vtt 806 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.en.vtt 804 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt 804 Bytes
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.zh-CN.vtt 804 Bytes
  • Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.zh-CN.vtt 801 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.en.vtt 797 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt 793 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt 793 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt 791 Bytes
  • Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.en.vtt 791 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt 790 Bytes
  • Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.zh-CN.vtt 787 Bytes
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.pt-BR.vtt 786 Bytes
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.ar.vtt 784 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.pt-BR.vtt 781 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.en.vtt 777 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt 777 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.en.vtt 775 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.pt-BR.vtt 773 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.en.vtt 772 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.pt-BR.vtt 772 Bytes
  • Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt 771 Bytes
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en-US.vtt 770 Bytes
  • Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.zh-CN.vtt 769 Bytes
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.ar.vtt 769 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt 769 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.en.vtt 768 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.ar.vtt 768 Bytes
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en.vtt 767 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.zh-CN.vtt 766 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.en-US.vtt 764 Bytes
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.pt-BR.vtt 760 Bytes
  • Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.zh-CN.vtt 756 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/29. Neural Networks Outro V2-pwA5shUkRVc.pt-BR.vtt 755 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.pt-BR.vtt 754 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.en.vtt 747 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.ar.vtt 745 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.zh-CN.vtt 744 Bytes
  • Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.zh-CN.vtt 742 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt 739 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.en.vtt 739 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.pt-BR.vtt 737 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.pt-BR.vtt 736 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.zh-CN.vtt 734 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.zh-CN.vtt 733 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.pt-BR.vtt 730 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.zh-CN.vtt 729 Bytes
  • Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.pt-BR.vtt 727 Bytes
  • Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt 727 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.en.vtt 725 Bytes
  • Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt 723 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.zh-CN.vtt 723 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.zh-CN.vtt 720 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt 719 Bytes
  • Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.zh-CN.vtt 718 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en-US.vtt 716 Bytes
  • Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt 716 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.pt-BR.vtt 716 Bytes
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.ar.vtt 711 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt 709 Bytes
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.pt-BR.vtt 707 Bytes
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.es-MX.vtt 707 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.en.vtt 707 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt 707 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt 705 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.en.vtt 702 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.en.vtt 701 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.zh-CN.vtt 701 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.ar.vtt 697 Bytes
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.pt-BR.vtt 694 Bytes
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.ar.vtt 694 Bytes
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.en.vtt 694 Bytes
  • Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.zh-CN.vtt 692 Bytes
  • Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt 690 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en.vtt 688 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.pt-BR.vtt 688 Bytes
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.en.vtt 685 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.pt-BR.vtt 683 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.pt-BR.vtt 683 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.ar.vtt 682 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.zh-CN.vtt 680 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.pt-BR.vtt 678 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt 678 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.zh-CN.vtt 677 Bytes
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.zh-CN.vtt 675 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.pt-BR.vtt 672 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.pt-BR.vtt 671 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.zh-CN.vtt 668 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.en.vtt 665 Bytes
  • Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.en.vtt 663 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt 663 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt 663 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.zh-CN.vtt 662 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt 657 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt.vtt 656 Bytes
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.pt-BR.vtt 655 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.zh-CN.vtt 655 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.en.vtt 644 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.pt-BR.vtt 643 Bytes
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt 643 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.pt-BR.vtt 638 Bytes
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.en-US.vtt 638 Bytes
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.en.vtt 635 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt 634 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.zh-CN.vtt 633 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.en.vtt 633 Bytes
  • Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt 631 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.ar.vtt 624 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt 624 Bytes
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.en.vtt 622 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt 618 Bytes
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt 615 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.en.vtt 613 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.zh-CN.vtt 612 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.ar.vtt 608 Bytes
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.en-US.vtt 608 Bytes
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.en.vtt 607 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt 607 Bytes
  • Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt 607 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.pt-BR.vtt 606 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.en.vtt 601 Bytes
  • Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.en.vtt 601 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.en.vtt 600 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt 600 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.pt-BR.vtt 599 Bytes
  • Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt 599 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.ar.vtt 597 Bytes
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.en.vtt 596 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.en.vtt 595 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt 594 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.zh-CN.vtt 593 Bytes
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.pt-BR.vtt 592 Bytes
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.zh-CN.vtt 590 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.pt-BR.vtt 590 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.zh-CN.vtt 589 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt 588 Bytes
  • Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.pt-BR.vtt 588 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.en.vtt 586 Bytes
  • Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.zh-CN.vtt 584 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt 584 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt 583 Bytes
  • Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.zh-CN.vtt 580 Bytes
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.en.vtt 579 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.pt-BR.vtt 574 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.en.vtt 573 Bytes
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.pt-BR.vtt 573 Bytes
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.ar.vtt 570 Bytes
  • Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.zh-CN.vtt 568 Bytes
  • Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.zh-CN.vtt 561 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.ar.vtt 561 Bytes
  • Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.zh-CN.vtt 560 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.ar.vtt 559 Bytes
  • Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.en.vtt 558 Bytes
  • Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt 557 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt 556 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt 555 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt 551 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt 551 Bytes
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.pt-BR.vtt 549 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt 548 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt 545 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt 543 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.ar.vtt 542 Bytes
  • Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.en.vtt 540 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt 540 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.zh-CN.vtt 540 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt 538 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt 538 Bytes
  • Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.zh-CN.vtt 535 Bytes
  • Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.pt-BR.vtt 533 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.zh-CN.vtt 530 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.en.vtt 526 Bytes
  • Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt 524 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.ar.vtt 521 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.pt-BR.vtt 518 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt 517 Bytes
  • Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.en.vtt 514 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt 514 Bytes
  • Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.pt-BR.vtt 512 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.ar.vtt 512 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.ar.vtt 510 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt 510 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt 508 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.zh-CN.vtt 507 Bytes
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.pt-BR.vtt 507 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.ar.vtt 505 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt 505 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt 501 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.en.vtt 501 Bytes
  • Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.zh-CN.vtt 499 Bytes
  • Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.zh-CN.vtt 498 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.pt-BR.vtt 497 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt 495 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.ar.vtt 490 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt 490 Bytes
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.en.vtt 489 Bytes
  • Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.zh-CN.vtt 488 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.zh-CN.vtt 488 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.zh-CN.vtt 487 Bytes
  • Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.zh-CN.vtt 485 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.en.vtt 483 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.pt-BR.vtt 482 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt 481 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.ar.vtt 479 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.pt-BR.vtt 478 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt 477 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.en.vtt 476 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt 475 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.pt-BR.vtt 474 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.en.vtt 473 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt 473 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.en.vtt 472 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.pt-BR.vtt 472 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.zh-CN.vtt 468 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt 467 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.en.vtt 466 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.pt-BR.vtt 465 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt 460 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.en.vtt 458 Bytes
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.en.vtt 457 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt 457 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.zh-CN.vtt 456 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.pt-BR.vtt 454 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.pt-BR.vtt 454 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt 453 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.en.vtt 451 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.ar.vtt 444 Bytes
  • Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.zh-CN.vtt 440 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.pt-BR.vtt 439 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt 438 Bytes
  • Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.zh-CN.vtt 437 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.en.vtt 435 Bytes
  • Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt 432 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.pt-BR.vtt 426 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.zh-CN.vtt 425 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.ar.vtt 425 Bytes
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.ar.vtt 425 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.zh-CN.vtt 424 Bytes
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.pt-BR.vtt 423 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.zh-CN.vtt 422 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.pt-BR.vtt 421 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt 420 Bytes
  • Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.pt-BR.vtt 420 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.en.vtt 419 Bytes
  • Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.zh-CN.vtt 419 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.en.vtt 419 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.en.vtt 418 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.pt-BR.vtt 410 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt 410 Bytes
  • Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.zh-CN.vtt 408 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.en.vtt 406 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.pt-BR.vtt 402 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.en.vtt 399 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.zh-CN.vtt 396 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.en.vtt 395 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.ar.vtt 393 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt 392 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt 390 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt 385 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt 385 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt 371 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt 370 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.pt-BR.vtt 369 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.zh-CN.vtt 369 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.en.vtt 368 Bytes
  • Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt 364 Bytes
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.pt-BR.vtt 362 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt 361 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.zh-CN.vtt 361 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.ar.vtt 360 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.ar.vtt 359 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.ar.vtt 357 Bytes
  • Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt 357 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.en.vtt 355 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.zh-CN.vtt 355 Bytes
  • Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.zh-CN.vtt 342 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.zh-CN.vtt 335 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt 332 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.pt-BR.vtt 331 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.pt-BR.vtt 326 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.pt-BR.vtt 326 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.en.vtt 325 Bytes
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.en.vtt 325 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt 324 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.en.vtt 320 Bytes
  • Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.zh-CN.vtt 316 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.en.vtt 315 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.en.vtt 312 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt 309 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.pt-BR.vtt 306 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.ar.vtt 306 Bytes
  • Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.zh-CN.vtt 305 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en.vtt 303 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.pt-BR.vtt 302 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.ar.vtt 301 Bytes
  • Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt 301 Bytes
  • Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.zh-CN.vtt 299 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.en.vtt 298 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.en.vtt 292 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.pt-BR.vtt 292 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.ar.vtt 284 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.ar.vtt 282 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt 277 Bytes
  • Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.zh-CN.vtt 277 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.en.vtt 273 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.pt-BR.vtt 271 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.ar.vtt 258 Bytes
  • Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.zh-CN.vtt 245 Bytes
  • Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.zh-CN.vtt 243 Bytes
  • How you can help Team-FTU.txt 241 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.pt-BR.vtt 233 Bytes
  • Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.zh-CN.vtt 232 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.pt-BR.vtt 230 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.en.vtt 229 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.ar.vtt 226 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.pt-BR.vtt 226 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.zh-CN.vtt 222 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.en.vtt 214 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.en.vtt 208 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.en.vtt 207 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.zh-CN.vtt 206 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.en.vtt 205 Bytes
  • Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.pt-BR.vtt 204 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.ar.vtt 204 Bytes
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.ar.vtt 203 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.pt-BR.vtt 186 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.pt-BR.vtt 180 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.ar.vtt 171 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.pt-BR.vtt 171 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.ar.vtt 168 Bytes
  • Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.zh-CN.vtt 167 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.zh-CN.vtt 166 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.zh-CN.vtt 166 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.zh-CN.vtt 165 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.pt-BR.vtt 164 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.en.vtt 164 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.pt-BR.vtt 143 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.en.vtt 141 Bytes
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.pt-BR.vtt 141 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.en.vtt 140 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.ar.vtt 140 Bytes
  • Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.en.vtt 139 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.en.vtt 138 Bytes
  • [FreeCoursesOnline.Me].url 133 Bytes
  • [FreeTutorials.Eu].url 129 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.zh-CN.vtt 125 Bytes
  • Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.zh-CN.vtt 125 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.pt-BR.vtt 124 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.ar.vtt 122 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.ar.vtt 118 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.zh-CN.vtt 113 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.en.vtt 109 Bytes
  • Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.pt-BR.vtt 109 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.en.vtt 108 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.zh-CN.vtt 107 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.pt-BR.vtt 105 Bytes
  • Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.en.vtt 104 Bytes

随机展示

相关说明

本站不存储任何资源内容，只收集BT种子元数据(例如文件名和文件大小)和磁力链接(BT种子标识符)，并提供查询服务，是一个完全合法的搜索引擎系统。 网站不提供种子下载服务，用户可以通过第三方链接或磁力链接获取到相关的种子资源。本站也不对BT种子真实性及合法性负责，请用户注意甄别！
>