吴恩达新书Machine Learning Yearing共享版

Page 2 Machine Learning Yearning-Draft Andrew Ng

Table of Contents (Draft)

1 Why Machine Learning Strategy

2 How to use this book to help your team

9 Optimizing and satisficing metrics

10 Having a dev set and metric speeds up iterations

11 When to change dev/test sets and metrics

12 Takeaways: Setting up development and test sets

13 Build your first system quickly, then iterate

14 Error analysis: Look at dev set examples to evaluate ideas

15 Evaluating multiple ideas in parallel during error analysis

16 Cleaning up mislabeled dev and test set examples

17 If you have a large dev set, split it into two subsets, only one of which you look at

18 How big should the Eyeball and Blackbox dev sets be?

25 Techniques for reducing avoidable bias

Page 3 Machine Learning Yearning-Draft Andrew Ng

26 Techniques for reducing Variance

27 Error analysis on the training set

28 Diagnosing bias and variance: Learning curves

29 Plotting training error

30 Interpreting learning curves: High bias

31 Interpreting learning curves: Other cases

32 Plotting learning curves

33 Why we compare to human-level performance

40 Generalizing from the training set to the dev set

41 Addressing Bias, and Variance, and Data Mismatch

42 Addressing data mismatch

43 Artificial data synthesis

44 The Optimization Verification test

45 General form of Optimization Verification test

46 Reinforcement learning example

47 The rise of end-to-end learning

48 More end-to-end learning examples

49 Pros and cons of end-to-end learning

55 Big picture

56 Credits