Machine Learning Yearning

Technical Strategy for AI Engineers, In the Era of Deep Learning

by Andrew Ng

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Book Description

AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.

This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. After reading Machine Learning Yearning, you will be able to:
- Prioritize the most promising directions for an AI project
- Diagnose errors in a machine learning system
- Build ML in complex settings, such as mismatched training/ test sets
- Set up an ML project to compare to and/or surpass human- level performance
- Know when and how to apply end-to-end learning, transfer learning, and multi-task learning.

This open book is licensed under a Creative Commons License (CC BY). You can download Machine Learning Yearning ebook for free in PDF format (4.1 MB).

Table of Contents

Chapter 1
Why Machine Learning Strategy
 
Chapter 2
How to use this book to help your team
 
Chapter 3
Prerequisites and Notation
 
Chapter 4
Scale drives machine learning progress
 
Chapter 5
Your development and test sets
 
Chapter 6
Your dev and test sets should come from the same distribution
 
Chapter 7
How large do the dev/test sets need to be?
 
Chapter 8
Establish a single-number evaluation metric for your team to optimize
 
Chapter 9
Optimizing and satisficing metrics
 
Chapter 10
Having a dev set and metric speeds up iterations
 
Chapter 11
When to change dev/test sets and metrics
 
Chapter 12
Takeaways: Setting up development and test sets
 
Chapter 13
Build your first system quickly, then iterate
 
Chapter 14
Error analysis: Look at dev set examples to evaluate ideas
 
Chapter 15
Evaluating multiple ideas in parallel during error analysis
 
Chapter 16
Cleaning up mislabeled dev and test set examples
 
Chapter 17
If you have a large dev set, split it into two subsets, only one of which you look at
 
Chapter 18
How big should the Eyeball and Blackbox dev sets be?
 
Chapter 19
Takeaways: Basic error analysis
 
Chapter 20
Bias and Variance: The two big sources of error
 
Chapter 21
Examples of Bias and Variance
 
Chapter 22
Comparing to the optimal error rate
 
Chapter 23
Addressing Bias and Variance
 
Chapter 24
Bias vs. Variance tradeoff
 
Chapter 25
Techniques for reducing avoidable bias
 
Chapter 26
Error analysis on the training set
 
Chapter 27
Techniques for reducing variance
 
Chapter 28
Diagnosing bias and variance: Learning curves
 
Chapter 29
Plotting training error
 
Chapter 30
Interpreting learning curves: High bias
 
Chapter 31
Interpreting learning curves: Other cases
 
Chapter 32
Plotting learning curves
 
Chapter 33
Why we compare to human-level performance
 
Chapter 34
How to define human-level performance
 
Chapter 35
Surpassing human-level performance
 
Chapter 36
When you should train and test on different distributions
 
Chapter 37
How to decide whether to use all your data
 
Chapter 38
How to decide whether to include inconsistent data
 
Chapter 39
Weighting data
 
Chapter 40
Generalizing from the training set to the dev set
 
Chapter 41
Identifying Bias, Variance, and Data Mismatch Errors
 
Chapter 42
Addressing data mismatch
 
Chapter 43
Artificial data synthesis
 
Chapter 44
The Optimization Verification test
 
Chapter 45
General form of Optimization Verification test
 
Chapter 46
Reinforcement learning example
 
Chapter 47
The rise of end-to-end learning
 
Chapter 48
More end-to-end learning examples
 
Chapter 49
Pros and cons of end-to-end learning
 
Chapter 50
Choosing pipeline components: Data availability
 
Chapter 51
Choosing pipeline components: Task simplicity
 
Chapter 52
Directly learning rich outputs
 
Chapter 53
Error analysis by parts
 
Chapter 54
Attributing error to one part
 
Chapter 55
General case of error attribution
 
Chapter 56
Error analysis by parts and comparison to human-level performance
 
Chapter 57
Spotting a flawed ML pipeline
 
Chapter 58
Building a superhero team - Get your teammates to read this
 

Book Details

Subject
Computer Science
Publisher
Self-publishing
Published
2018
Pages
118
Edition
1
Language
English
PDF Size
4.1 MB
License
CC BY

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