[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python

磁力链接/BT种子名称

[Tutorialsplanet.NET] Udemy - Deep Learning Prerequisites Linear Regression in Python

磁力链接/BT种子简介

种子哈希：484708b8ac5470a7a19c3327077bf4780f2ddb05
文件大小： 1.08G
已经下载：69次
下载速度：极快
收录时间：2021-04-18
最近下载：2024-12-09

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:484708B8AC5470A7A19C3327077BF4780F2DDB05
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

  • 6. Setting Up Your Environment/1. Windows-Focused Environment Setup 2018.mp4 195.3 MB
  • 7. Extra Help With Python Coding for Beginners/3. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
  • 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 63.2 MB
  • 1. Welcome/5. Statistics vs. Machine Learning.mp4 58.2 MB
  • 1. Welcome/1. Welcome.mp4 52.1 MB
  • 6. Setting Up Your Environment/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 46.0 MB
  • 8. Effective Learning Strategies for Machine Learning/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
  • 9. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
  • 8. Effective Learning Strategies for Machine Learning/4. What order should I take your courses in (part 2).mp4 39.5 MB
  • 8. Effective Learning Strategies for Machine Learning/3. What order should I take your courses in (part 1).mp4 30.7 MB
  • 1. Welcome/4. How to Succeed in this Course.mp4 29.3 MB
  • 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25.9 MB
  • 7. Extra Help With Python Coding for Beginners/1. How to Code by Yourself (part 1).mp4 25.7 MB
  • 4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 24.6 MB
  • 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23.9 MB
  • 2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.mp4 21.2 MB
  • 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 20.7 MB
  • 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 20.3 MB
  • 8. Effective Learning Strategies for Machine Learning/1. How to Succeed in this Course (Long Version).mp4 19.2 MB
  • 2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.mp4 18.3 MB
  • 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 18.1 MB
  • 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4 17.2 MB
  • 2. 1-D Linear Regression Theory and Code/11. Suggestion Box.mp4 16.9 MB
  • 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 15.6 MB
  • 7. Extra Help With Python Coding for Beginners/2. How to Code by Yourself (part 2).mp4 15.5 MB
  • 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp4 15.1 MB
  • 4. Practical machine learning issues/2. Interpreting the Weights.mp4 14.8 MB
  • 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4 12.9 MB
  • 4. Practical machine learning issues/1. What do all these letters mean.mp4 10.1 MB
  • 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp4 8.9 MB
  • 1. Welcome/3. What is machine learning How does linear regression play a role.mp4 8.8 MB
  • 4. Practical machine learning issues/15. L1 Regularization - Code.mp4 8.7 MB
  • 4. Practical machine learning issues/5. Categorical inputs.mp4 8.6 MB
  • 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp4 8.5 MB
  • 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp4 8.5 MB
  • 4. Practical machine learning issues/9. L2 Regularization - Code.mp4 8.5 MB
  • 7. Extra Help With Python Coding for Beginners/4. Python 2 vs Python 3.mp4 8.2 MB
  • 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp4 7.5 MB
  • 4. Practical machine learning issues/8. L2 Regularization - Theory.mp4 7.0 MB
  • 1. Welcome/2. Introduction and Outline.mp4 6.6 MB
  • 4. Practical machine learning issues/10. The Dummy Variable Trap.mp4 6.4 MB
  • 9. Appendix FAQ/1. What is the Appendix.mp4 5.7 MB
  • 4. Practical machine learning issues/16. L1 vs L2 Regularization.mp4 5.0 MB
  • 4. Practical machine learning issues/14. L1 Regularization - Theory.mp4 4.9 MB
  • 2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp4 4.7 MB
  • 2. 1-D Linear Regression Theory and Code/7. Introduction to Moore's Law Problem.mp4 4.6 MB
  • 4. Practical machine learning issues/3. Generalization error, train and test sets.mp4 4.6 MB
  • 4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp4 4.0 MB
  • 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp4 3.7 MB
  • 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp4 3.7 MB
  • 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4 3.2 MB
  • 2. 1-D Linear Regression Theory and Code/10. R-squared Quiz 1.mp4 2.9 MB
  • 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp4 1.1 MB
  • 8. Effective Learning Strategies for Machine Learning/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 34.6 kB
  • 8. Effective Learning Strategies for Machine Learning/4. What order should I take your courses in (part 2).srt 25.8 kB
  • 7. Extra Help With Python Coding for Beginners/1. How to Code by Yourself (part 1).srt 24.8 kB
  • 6. Setting Up Your Environment/1. Windows-Focused Environment Setup 2018.srt 22.2 kB
  • 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).srt 18.1 kB
  • 8. Effective Learning Strategies for Machine Learning/3. What order should I take your courses in (part 1).srt 17.6 kB
  • 1. Welcome/5. Statistics vs. Machine Learning.srt 16.4 kB
  • 6. Setting Up Your Environment/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.9 kB
  • 8. Effective Learning Strategies for Machine Learning/1. How to Succeed in this Course (Long Version).srt 15.6 kB
  • 7. Extra Help With Python Coding for Beginners/3. Proof that using Jupyter Notebook is the same as not using it.srt 15.4 kB
  • 7. Extra Help With Python Coding for Beginners/2. How to Code by Yourself (part 2).srt 14.3 kB
  • 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.srt 13.2 kB
  • 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.srt 11.3 kB
  • 4. Practical machine learning issues/17. Why Divide by Square Root of D.srt 9.6 kB
  • 1. Welcome/4. How to Succeed in this Course.srt 9.5 kB
  • 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.srt 9.4 kB
  • 4. Practical machine learning issues/1. What do all these letters mean.srt 8.7 kB
  • 9. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 8.6 kB
  • 2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.srt 8.3 kB
  • 2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.srt 7.1 kB
  • 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.srt 6.9 kB
  • 7. Extra Help With Python Coding for Beginners/4. Python 2 vs Python 3.srt 6.7 kB
  • 4. Practical machine learning issues/8. L2 Regularization - Theory.srt 6.1 kB
  • 4. Practical machine learning issues/11. Gradient Descent Tutorial.srt 6.1 kB
  • 4. Practical machine learning issues/10. The Dummy Variable Trap.srt 6.0 kB
  • 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.srt 6.0 kB
  • 1. Welcome/2. Introduction and Outline.srt 6.0 kB
  • 1. Welcome/3. What is machine learning How does linear regression play a role.srt 6.0 kB
  • 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.srt 5.8 kB
  • 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.srt 5.6 kB
  • 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.srt 5.6 kB
  • 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.srt 5.5 kB
  • 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).srt 5.4 kB
  • 2. 1-D Linear Regression Theory and Code/11. Suggestion Box.srt 5.0 kB
  • 4. Practical machine learning issues/5. Categorical inputs.srt 4.9 kB
  • 1. Welcome/1. Welcome.srt 4.9 kB
  • 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.srt 4.8 kB
  • 4. Practical machine learning issues/2. Interpreting the Weights.srt 4.7 kB
  • 4. Practical machine learning issues/14. L1 Regularization - Theory.srt 4.6 kB
  • 4. Practical machine learning issues/16. L1 vs L2 Regularization.srt 4.6 kB
  • 9. Appendix FAQ/1. What is the Appendix.srt 4.0 kB
  • 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.srt 3.9 kB
  • 4. Practical machine learning issues/15. L1 Regularization - Code.srt 3.9 kB
  • 2. 1-D Linear Regression Theory and Code/7. Introduction to Moore's Law Problem.srt 3.8 kB
  • 4. Practical machine learning issues/9. L2 Regularization - Code.srt 3.7 kB
  • 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.srt 3.5 kB
  • 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.srt 3.0 kB
  • 4. Practical machine learning issues/3. Generalization error, train and test sets.srt 2.9 kB
  • 4. Practical machine learning issues/6. One-Hot Encoding Quiz.srt 2.8 kB
  • 2. 1-D Linear Regression Theory and Code/10. R-squared Quiz 1.srt 2.4 kB
  • 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.srt 2.1 kB
  • 2. 1-D Linear Regression Theory and Code/6. R-squared in code.srt 1.9 kB
  • 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.srt 1.7 kB
  • 1. Welcome/6. What can linear regression be used for.html 150 Bytes
  • [Tutorialsplanet.NET].url 128 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！
>