机器学习基石

磁力链接/BT种子名称

机器学习基石

磁力链接/BT种子简介

种子哈希：b3d3be1d43a9d4ea6d7b32d8d13527057315eb52
文件大小： 1.15G
已经下载：3次
下载速度：极快
收录时间：2017-09-25
最近下载：2024-10-13

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文件列表

  • homeworks/discusstion/MLF_hw1.zip 24.0 kB
  • homeworks/discusstion/机器学习基石心得1 By Loveisp.docx 962.8 kB
  • homeworks/discusstion/机器学习基石心得3-第一次作业下.docx 120.0 kB
  • homeworks/Homework #0.pdf 121.8 kB
  • homeworks/Homework #1/data.xls 140.3 kB
  • homeworks/Homework #1/error_date.m 249 Bytes
  • homeworks/Homework #1/hw1.dat 14.1 kB
  • homeworks/Homework #1/hw2.dat 17.8 kB
  • homeworks/Homework #1/hw3.dat 17.9 kB
  • homeworks/Homework #1/panbieshi.m 114 Bytes
  • homeworks/Homework #1/PLA_v1.m 404 Bytes
  • homeworks/Homework #1/PLA_v2.m 1.0 kB
  • homeworks/Homework #1/PLA_v3.m 445 Bytes
  • homeworks/Homework #1/Pocket.m 1.5 kB
  • homeworks/Homework #1/一些指令.txt 219 Bytes
  • homeworks/Homework #2/data.xlsx 107.8 kB
  • homeworks/Homework #2/data_test.dat 60.6 kB
  • homeworks/Homework #2/data_train.dat 6.1 kB
  • homeworks/Homework #2/draft1.m 2.1 kB
  • homeworks/Homework #2/draft2.m 2.0 kB
  • homeworks/Homework #2/test.m 2.5 kB
  • homeworks/Homework #2/test20.m 633 Bytes
  • homeworks/Homework #2/计算过程.xlsx 14.4 kB
  • homeworks/Homework #3/ML/data.xlsx 107.8 kB
  • homeworks/Homework #3/ML/data_test.dat 60.6 kB
  • homeworks/Homework #3/ML/data_train.dat 6.1 kB
  • homeworks/Homework #3/ML/draft1.m 2.1 kB
  • homeworks/Homework #3/ML/draft2.m 2.0 kB
  • homeworks/Homework #3/ML/hw13.m 1.0 kB
  • homeworks/Homework #3/ML/hw14.m 1.6 kB
  • homeworks/Homework #3/ML/hw18.m 1.1 kB
  • homeworks/Homework #3/ML/hw3_Ein_d.m 784 Bytes
  • homeworks/Homework #3/ML/hw3_test.dat 490.1 kB
  • homeworks/Homework #3/ML/hw3_train.dat 163.4 kB
  • homeworks/Homework #3/ML/hw7.m 417 Bytes
  • homeworks/Homework #3/ML/hw9.m 612 Bytes
  • homeworks/Homework #3/ML/test.m 2.5 kB
  • homeworks/Homework #3/ML/test20.m 633 Bytes
  • homeworks/Homework #3/ntumlone-hw3-hw3_test.dat 490.1 kB
  • homeworks/Homework #3/ntumlone-hw3-hw3_train.dat 163.4 kB
  • homeworks/Homework #3/QQ图片20140113175847.jpg 163.2 kB
  • homeworks/Homework #3/作业中较难的那几题的参考材料.zip 416.8 kB
  • homeworks/Homework #3/理解newton direction.pdf 277.1 kB
  • homeworks/Homework #4/HW4_13.m 1.6 kB
  • homeworks/Homework #4/HW4_15.m 1.5 kB
  • homeworks/Homework #4/HW4_19.m 4.8 kB
  • homeworks/Homework #4/hw4_test.dat 20.5 kB
  • homeworks/Homework #4/hw4_train.dat 4.1 kB
  • homeworks/Homework #4/w_reg.m 180 Bytes
  • Learning From Data 2nd Ed (Wiley,2007).pdf 4.2 MB
  • ppt/lecture_slides-01_handout.pdf 6.8 MB
  • ppt/lecture_slides-02_handout.pdf 819.5 kB
  • ppt/lecture_slides-03_handout.pdf 836.0 kB
  • ppt/lecture_slides-04_handout.pdf 1.9 MB
  • ppt/lecture_slides-05_handout .pdf 456.9 kB
  • ppt/lecture_slides-06_handout.pdf 570.9 kB
  • ppt/lecture_slides-07_handout.pdf 498.7 kB
  • ppt/lecture_slides-08_handout.pdf 960.0 kB
  • ppt/lecture_slides-09_handout.pdf 818.7 kB
  • ppt/lecture_slides-10_handout.pdf 538.7 kB
  • ppt/lecture_slides-11_handout.pdf 619.3 kB
  • ppt/lecture_slides-12_handout.pdf 1.3 MB
  • ppt/lecture_slides-13_handout.pdf 838.0 kB
  • ppt/lecture_slides-14_handout.pdf 899.5 kB
  • ppt/lecture_slides-15_handout.pdf 1.1 MB
  • ppt/lecture_slides-16_handout.pdf 5.1 MB
  • video/1 - 1 - Course Introduction (10-58).mp4 14.5 MB
  • video/1 - 2 - What is Machine Learning (18-28).mp4 16.7 MB
  • video/1 - 3 - Applications of Machine Learning (18-56).mp4 23.4 MB
  • video/1 - 4 - Components of Machine Learning (11-45).mp4 11.2 MB
  • video/1 - 5 - Machine Learning and Other Fields (10-21).mp4 12.6 MB
  • video/10 - 1 - Logistic Regression Problem (14-33).mp4 12.5 MB
  • video/10 - 2 - Logistic Regression Error (15-58).mp4 12.5 MB
  • video/10 - 3 - Gradient of Logistic Regression Error (15-38).mp4 13.0 MB
  • video/10 - 4 - Gradient Descent (19-18).mp4 15.6 MB
  • video/11 - 1 - Linear Models for Binary Classification (21-35).mp4 17.7 MB
  • video/11 - 2 - Stochastic Gradient Descent (11-39).mp4 10.4 MB
  • video/11 - 3 - Multiclass via Logistic Regression (14-18).mp4 11.8 MB
  • video/11 - 4 - Multiclass via Binary Classification (11-35).mp4 9.8 MB
  • video/12 - 1 - Quadratic Hypothesis (23-47).mp4 18.8 MB
  • video/12 - 2 - Nonlinear Transform (09-52).mp4 8.4 MB
  • video/12 - 3 - Price of Nonlinear Transform (15-37).mp4 13.2 MB
  • video/12 - 4 - Structured Hypothesis Sets (09-36).mp4 7.7 MB
  • video/13 - 1 - What is Overfitting- (10-45).mp4 9.4 MB
  • video/13 - 2 - The Role of Noise and Data Size (13-36).mp4 12.0 MB
  • video/13 - 3 - Deterministic Noise (14-07).mp4 12.5 MB
  • video/13 - 4 - Dealing with Overfitting (10-49).mp4 9.2 MB
  • video/14 - 1 - Regularized Hypothesis Set (19-16).mp4 15.9 MB
  • video/14 - 2 - Weight Decay Regularization (24-08).mp4 19.4 MB
  • video/14 - 3 - Regularization and VC Theory (08-15).mp4 7.5 MB
  • video/14 - 4 - General Regularizers (13-28).mp4 11.8 MB
  • video/15 - 1 - Model Selection Problem (16-00).mp4 13.9 MB
  • video/15 - 2 - Validation (13-24).mp4 11.0 MB
  • video/15 - 3 - Leave-One-Out Cross Validation (16-06).mp4 12.9 MB
  • video/15 - 4 - V-Fold Cross Validation (10-41).mp4 9.6 MB
  • video/16 - 1 - Occam-'s Razor (10-08).mp4 8.6 MB
  • video/16 - 2 - Sampling Bias (11-50).mp4 10.8 MB
  • video/16 - 3 - Data Snooping (12-28).mp4 11.3 MB
  • video/16 - 4 - Power of Three (08-49).mp4 7.9 MB
  • video/2 - 1 - Perceptron Hypothesis Set (15-42).mp4 19.4 MB
  • video/2 - 2 - Perceptron Learning Algorithm (PLA) (19-46).mp4 17.4 MB
  • video/2 - 3 - Guarantee of PLA (12-37).mp4 15.2 MB
  • video/2 - 4 - Non-Separable Data (12-55).mp4 35.4 MB
  • video/3 - 1 - Learning is Impossible- (13-32).mp4 55.0 MB
  • video/3 - 2 - Probability to the Rescue (11-33).mp4 48.6 MB
  • video/3 - 3 - Connection to Learning (16-46).mp4 76.2 MB
  • video/3 - 4 - Connection to Real Learning (18-06).mp4 82.7 MB
  • video/4 - 1 - Learning with Different Output Space (17-26).mp4 80.0 MB
  • video/4 - 2 - Learning with Different Data Label (18-12).mp4 52.6 MB
  • video/4 - 3 - Learning with Different Protocol (11-09).mp4 32.9 MB
  • video/4 - 4 - Learning with Different Input Space (14-13).mp4 42.9 MB
  • video/5 - 1 - Recap and Preview (13-44).mp4 11.9 MB
  • video/5 - 2 - Effective Number of Lines (15-26).mp4 13.2 MB
  • video/5 - 3 - Effective Number of Hypotheses (16-17).mp4 13.8 MB
  • video/5 - 4 - Break Point (07-44).mp4 6.9 MB
  • video/6 - 1 - Restriction of Break Point (14-18).mp4 12.1 MB
  • video/6 - 2 - Bounding Function- Basic Cases (06-56).mp4 5.8 MB
  • video/6 - 3 - Bounding Function- Inductive Cases (14-47).mp4 12.2 MB
  • video/6 - 4 - A Pictorial Proof (16-01).mp4 13.5 MB
  • video/7 - 1 - Definition of VC Dimension (13-10).mp4 11.2 MB
  • video/7 - 2 - VC Dimension of Perceptrons (13-27).mp4 10.5 MB
  • video/7 - 3 - Physical Intuition of VC Dimension (6-11).mp4 5.4 MB
  • video/7 - 4 - Interpreting VC Dimension (17-13).mp4 14.2 MB
  • video/8 - 1 - Noise and Probabilistic Target (17-01).mp4 14.6 MB
  • video/8 - 2 - Error Measure (15-10).mp4 12.0 MB
  • video/8 - 3 - Algorithmic Error Measure (13-46).mp4 11.5 MB
  • video/8 - 4 - Weighted Classification (16-54).mp4 13.7 MB
  • video/9 - 1 - Linear Regression Problem (10-08).mp4 8.4 MB
  • video/9 - 2 - Linear Regression Algorithm (20-03).mp4 15.2 MB
  • video/9 - 3 - Generalization Issue (20-34).mp4 16.0 MB
  • video/9 - 4 - Linear Regression for Binary Classification (11-23).mp4 9.5 MB
  • 延伸阅读.doc 34.8 kB

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