书籍推荐：Machine Learning Yearning
Machine Learning Yearning(机器学习思维)是NG的新书，不过这本书的重点不在于教授ML算法，而在于教你如何使ML算法发挥作用。很多AI课程会给教你制造一个锤子; 这本书教你如何使用锤子。如果你渴望成为AI的技术领导者并想学习如何为你的团队设定方向，这本书将会有所帮助。
建议程序员入门学习AI时最好先看看这本书，不然可能会陷入自己的程序思维。当然，这本书的内容其实并不多，每一章节内容都是二三页，都是对从业人员的一些宝贵指导以及建议，千万不要小看这些经验建议，这可能会给你的项目节约很长的实验时间。
自学能力强的迫不及待的童鞋们，我已经为你们找链接，如果没有系统的时间看的童鞋们，也没关系，我正打算看看此书，每天都会分享书籍内容以及自己的阅读笔记，为了省时间大可每天花五分钟零散时间跟着推文一起看，觉得有帮助也可分享给周围需要的朋友~~
官网地址：http://www.mlyearning.org/
这里也有gitbook的一个翻译版：https://xiaqunfeng.gitbooks.io/machine-learning-yearning/content/
阅读Machine Learning Yearning后，您将能够：
1.优先考虑AI项目最有前途的方向
2.诊断机器学习系统中的错误
3.在复杂设置中构建ML，例如不匹配的训练/测试集
4.建立一个ML项目来比较和/或超越人类的表现
5.了解何时以及如何应用端到端学习，转移学习和多任务学习
首先让我们来看一下目录：
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, 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
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
觉得找资源麻烦的童鞋~~可以关注我微信公众号（然后后台发送关键词"NGxinshu"获取NG原手稿全部资源）或者直接知乎私信我，看到后会立马回复你。
接下来让我们一起学习吧~-~
参考:
1.http://www.mlyearning.org/
更多个人笔记请关注：
知乎专栏：https://www.zhihu.com/people/yuquanle/columns
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