Machine+Learning+Yearing-1-52

deeplearning.ai project.

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Table of Contents (Draft)

1 Why Machine Learning Strategy

2 How to use this book to help your team

3 Prerequisites and Notation

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

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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

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

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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

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