Free New Book by Andrew Ng: Machine Learning Yearning

This is the new book by Andrew Ng, still in progress. Andrew Yan-Tak Ng is a computer scientist and entrepreneur. He is one of the most influential minds in Artificial Intelligence and Deep Learning. Ng founded and led Google Brain and was a former VP & Chief Scientist at Baidu, building the company’s Artificial Intelligence Group into several thousand people. He is an adjunct professor (formerly associate professor and Director of the AI Lab) at Stanford University. Ng is also an early pioneer in online learning – which led to the co-founding of Coursera.

Machine Learning Yearning is a deeplearning.ai project. The subtitle of the book is Technical strategy for AI engineers in the era of deep learning.

Content of the book

1. Why Machine Learning Strategy
2. How to use this book to help your team
3. Prerequisites and Notation
4. Scale drives machine learning progress
5. Your development and test sets
6. Your dev and test sets should come from the same distribution
7. How large do the dev/test sets need to be?
8. Establish a single-number evaluation metric for your team to optimize
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?
19. Takeaways: Basic error analysis
20. Bias and Variance: The two big sources of error
21. Examples of Bias and Variance
22. Comparing to the optimal error rate
23. Addressing Bias and Variance
24. Bias vs. Variance tradeoff
25. Techniques for reducing avoidable bias
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
34. How to define human-level performance
35. Surpassing human-level performance
36. Why train and test on different distributions
37. Whether to use all your data
38. Whether to include inconsistent data
39. Weighting data
40. Generalizing from the training set to the dev set
41. Addressing Bias and Variance
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
50. Learned sub-components
51. Directly learning rich outputs
52. Error Analysis by Parts
53. Beyond supervised learning: What’s next?
54. Building a superhero team – Get your teammates to read this
55. Big picture
56. Credits

You can find more about this book project, and access the chapters, here (on Github). To get updates, visit this website. 

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