Table of Contents
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
Page 4 Machine Learning Yearning-Draft Andrew Ng
