yangxueruivs/machine-learning-yearning-translate

吴恩达新书翻译1-58章中英文对照

machine-learning-yearning-translate

Chinese translation of Andrew Ng's new book "Machine learning yearning". 已经有很多人在做同样的工作了，但我想自己试试独立完成翻译一本技术书籍。

目录 Table of Contents

1. 为什么使用机器学习 Why Machine Learning Strategy
2. 如何使用本书来帮助你的团队 How to use this book to help your team
3. 预备知识 Prerequisites and Notation
4. 规模驱动的机器学习进步 Scale drives machine learning progress
5. 验证集和测试集 Your development and test sets
6. 验证集和测试集需要满足相同的分布 Your dev and test sets should come from the same distribution
7. 如何设置验证/测试集的大小 How large do the dev/test sets need to be?
8. 建立用于优化的单一评估指标 Establish a single-number evaluation metric for your team to optimize
9. 优化指标 Optimizing and satisficing metrics
10. 验证集和评估指标可以加速迭代 Having a dev set and metric speeds up iterations
11. 如何修改验证/测试集和评估指标 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. Error analysis on the training set
27. Techniques for reducing variance
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. When you should train and test on different distributions
37. How to decide whether to use all your data
38. How to decide whether to include inconsistent data
39. Weighting data
40. Generalizing from the training set to the dev set
41. Identifying Bias, Variance, and Data Mismatch Errors
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. Choosing pipeline components: Data availability
51. Choosing pipeline components: Task simplicity
52. Directly learning rich outputs
53. Error analysis by parts
54. Attributing error to one part
55. General case of error attribution
56. Error analysis by parts and comparison to human-level performance
57. Spotting a flawed ML pipeline
58. Building a superhero team - Get your teammates to read this