《MACHINE LEARNING YEARNING》翻译——开篇

《MACHINE LEARNING YEARNING》是Andrew NG最近出的本新书，目前正在陆续发布书的手稿。打算翻译一下这本书，并借机梳理一下机器学习方面的知识。翻译中的任何不足之处，欢迎大家不吝指出。

Table of Contents (draft)目录
1. Why Machine Learning Strategy 为什么需要机器学习策略
2. How to use this book to help your team 如何使用这本书来帮助你的团队
3. Prerequisites and Notation 预备知识和符号约定
4. Scale drives machine learning progress 规模促进了机器学习的发展
5. Your development and test sets 你的开发集和测试集
6. Your dev and test sets should come from the same distribution 你的开发集和测试机应该来自同一分布
7. How large do the dev/test sets need to be? 开发集和测试集多大合适
8. Establish a single-number evaluation metric for your team to optimize 为你的团队进行算法优化建立一个单一数字的评估指标
9. Optimizing and satisficing metrics 优化指标和满足指标
10. Having a dev set and metric speeds up iterations 有一个开发集和评估指标来加速迭代
11. When to change dev/test sets and metrics 何时更改开发/测试集和评估指标
12. Takeaways: Setting up development and test sets 小结：建立开发集和测试集
13. Build your first system quickly, then iterate
14. Error analysis: Look at dev set examples to evaluate ideas 错误分析：查看开发集样本来评估idea
15. Evaluate multiple ideas in parallel during error analysis 错误分析时并行评估多个想法
16. If you have a large dev set, split it into two subsets, only one of which you look at
17. How big should the Eyeball and Blackbox dev sets be?
18. Takeaways: Basic error analysis
19. Bias and Variance: The two big sources of error
20. Examples of Bias and Variance
21. Comparing to the optimal error rate
22. Addressing Bias and Variance
23. Bias vs. Variance tradeoff
24. Techniques for reducing avoidable bias
25. Techniques for reducing Variance
26. Error analysis on the training set
27. Diagnosing bias and variance: Learning curves
28. Plotting training error
29. Interpreting learning curves: High bias
30. Interpreting learning curves: Other cases
31. Plotting learning curves
32. Why we compare to human-level performance
33. How to define human-level performance
34. Surpassing human-level performance
35. Why train and test on different distributions
36. Whether to use all your data
37. Whether to include inconsistent data
38. Weighting data
39. Generalizing from the training set to the dev set
40. Addressing Bias and Variance
41. Addressing data mismatch
42. Artificial data synthesis
43. The Optimization Verification test
44. General form of Optimization Verification test
45. Reinforcement learning example
46. The rise of end-to-end learning
47. More end-to-end learning examples
48. Pros and cons of end-to-end learning
49. Learned sub-components
50. Directly learning rich outputs
51. Error Analysis by Parts
52. Beyond supervised learning: What’s next?
53. Building a superhero team - Get your teammates to read this
54. Big picture
55. Credits

书稿下载
Machine_Learning_Yearning_V0.5_01.pdf
Machine_Learning_Yearning_V0.5_02.pdf
Machine_Learning_Yearning_V0.5_03.pdf