Machine Learning Yearning(機器學習思維)是NG的新書，不過這本書的重點不在於教授ML算法，而在於教你如何使ML算法發揮作用。 很多AI課程會給教你製造一個錘子; 這本書教你如何使用錘子。 如果你渴望成為AI的技術領導者並想學習如何為你的團隊設定方向，這本書將會有所幫助。
建議程序員入門學習AI時最好先看看這本書，不然可能會陷入自己的程序思維。當然，這本書的內容其實並不多，每一章節內容都是二三頁，都是對從業人員的一些寶貴指導以及建議，千萬不要小看這些經驗建議，這可能會給你的項目節約很長的實驗時間。
自學能力強的迫不及待的童鞋們，我已經為你們找鏈接，如果沒有系統的時間看的童鞋們，也沒關係，我正打算看看此書，每天都會分享書籍內容以及自己的閱讀筆記，為了省時間大可每天花五分鐘零散時間跟著推文一起看，覺得有幫助也可分享給周圍需要的朋友~~
- 官網地址：http://www.mlyearning.org/
- 這裡也有gitbook的一個翻譯版：https://xiaqunfeng.gitbooks.io/machine-learning-yearning/content/
閱讀Machine Learning Yearning後，您將能夠：
1.優先考慮AI項目最有前途的方向
2.診斷機器學習系統中的錯誤
3.在複雜設置中構建ML，例如不匹配的訓練/測試集
4.建立一個ML項目來比較和/或超越人類的表現
5.瞭解何時以及如何應用端到端學習，轉移學習和多任務學習
首先讓我們來看一下目錄：
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
覺得找資源麻煩的童鞋~~可以關注我微信公眾號（然後後臺發送關鍵詞"NGxinshu"獲取NG原手稿全部資源）或者直接知乎私信我，看到後會立馬回覆你。
接下來讓我們一起學習吧~-~
參考:
1.http://www.mlyearning.org/
更多個人筆記請關注：
公眾號：StudyForAI（小白人工智能入門學習）
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