- Introduction
- 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 Evaluate 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