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% * input email information at: https://www.deeplearning.ai/machine-learning-yearning/ |
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% * using url from email, download 'Ng-MLY01-13.pdf' |
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% * store this file as 'out.tex', and compile as 'pdflatex out.tex' |
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% 'Machine Learning Yearning - Andrew Ng.pdf' |
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\documentclass{article} |
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\usepackage[utf8]{inputenc} |
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\usepackage{geometry} |
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\usepackage{pdfpages} |
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\usepackage[ |
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pdfpagelabels=true, |
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pdftitle={Machine Learning Yearning (Draft Version)}, |
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pdfauthor={Andrew Ng}, |
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pdfsubject={Machine Learning}, |
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pdfkeywords={machine learning}, |
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unicode=true, |
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]{hyperref} |
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\usepackage{bookmark} |
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\begin{document} |
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\pagenumbering{arabic} |
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\setcounter{page}{1} |
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\includepdf[pages=1-]{Ng-MLY01-13.pdf} |
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\bookmark[page=1,level=0]{Cover} |
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\bookmark[page=3,level=0]{Table of Contents} |
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\bookmark[page=6,level=0]{1 Why Machine Learning Strategy} |
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\bookmark[page=8,level=0]{2 How to use this book to help your team} |
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\bookmark[page=9,level=0]{3 Prerequisites and Notation} |
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\bookmark[page=10,level=0]{4 Scale drives machine learning progress} |
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\bookmark[page=14,level=0]{5 Your development and test sets} |
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\bookmark[page=17,level=0]{6 Your dev and test sets should come from the same distribution} |
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\bookmark[page=19,level=0]{7 How large do the dev/test sets need to be?} |
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\bookmark[page=20,level=0]{8 Establish a single-number evaluation metric for your team to optimize} |
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\bookmark[page=22,level=0]{9 Optimizing and satisficing metrics} |
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\bookmark[page=24,level=0]{10 Having a dev set and metric speeds up iterations} |
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\bookmark[page=25,level=0]{11 When to change dev/test sets and metrics} |
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\bookmark[page=27,level=0]{12 Takeaways: Setting up development and test sets} |
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\bookmark[page=29,level=0]{13 Build your first system quickly, then iterate} |
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\bookmark[page=30,level=0]{14 Error analysis: Look at dev set examples to evaluate ideas} |
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\bookmark[page=32,level=0]{15 Evaluating multiple ideas in parallel during error analysis} |
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\bookmark[page=34,level=0]{16 Cleaning up mislabeled dev and test set examples} |
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\bookmark[page=36,level=0]{17 If you have a large dev set, split it into two subsets, only one of which you look at} |
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\bookmark[page=38,level=0]{18 How big should the Eyeball and Blackbox dev sets be?} |
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\bookmark[page=40,level=0]{19 Takeaways: Basic error analysis} |
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\bookmark[page=42,level=0]{20 Bias and Variance: The two big sources of error} |
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\bookmark[page=44,level=0]{21 Examples of Bias and Variance} |
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\bookmark[page=46,level=0]{22 Comparing to the optimal error rate} |
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\bookmark[page=49,level=0]{23 Addressing Bias and Variance} |
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\bookmark[page=50,level=0]{24 Bias vs. Variance tradeoff} |
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\bookmark[page=51,level=0]{25 Techniques for reducing avoidable bias} |
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\bookmark[page=52,level=0]{26 Error analysis on the training set} |
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\bookmark[page=53,level=0]{27 Techniques for reducing variance} |
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\bookmark[page=56,level=0]{28 Diagnosing bias and variance: Learning curves} |
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\bookmark[page=59,level=0]{29 Plotting training error} |
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\bookmark[page=60,level=0]{30 Interpreting learning curves: High bias} |
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\bookmark[page=62,level=0]{31 Interpreting learning curves: Other cases} |
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\bookmark[page=63,level=0]{32 Plotting learning curves} |
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\bookmark[page=66,level=0]{33 Why we compare to human-level performance} |
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\bookmark[page=68,level=0]{34 How to define human-level performance} |
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\bookmark[page=69,level=0]{35 Surpassing human-level performance} |
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\bookmark[page=71,level=0]{36 When you should train and test on different distributions} |
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\bookmark[page=73,level=0]{37 How to decide whether to use all your data} |
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\bookmark[page=75,level=0]{38 How to decide whether to include inconsistent data} |
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\bookmark[page=76,level=0]{39 Weighting data} |
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\bookmark[page=77,level=0]{40 Generalizing from the training set to the dev set} |
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\bookmark[page=79,level=0]{41 Identifying Bias, Variance, and Data Mismatch Errors} |
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\bookmark[page=81,level=0]{42 Addressing data mismatch} |
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\bookmark[page=82,level=0]{43 Artificial data synthesis} |
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\bookmark[page=85,level=0]{44 The Optimization Verification test} |
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\bookmark[page=87,level=0]{45 General form of Optimization Verification test} |
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\bookmark[page=88,level=0]{46 Reinforcement learning example} |
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\bookmark[page=91,level=0]{47 The rise of end-to-end learning} |
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\bookmark[page=93,level=0]{48 More end-to-end learning examples} |
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\bookmark[page=95,level=0]{49 Pros and cons of end-to-end learning} |
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\bookmark[page=97,level=0]{50 Choosing pipeline components: Data availability} |
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\bookmark[page=99,level=0]{51 Choosing pipeline components: Task simplicity} |
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\bookmark[page=103,level=0]{52 Directly learning rich outputs} |
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\bookmark[page=106,level=0]{53 Error analysis by parts} |
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\bookmark[page=109,level=0]{54 Attributing error to one part} |
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\bookmark[page=111,level=0]{55 General case of error attribution} |
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\bookmark[page=113,level=0]{56 Error analysis by parts and comparison to human-level performance} |
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\bookmark[page=115,level=0]{57 Spotting a flawed ML pipeline} |
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\bookmark[page=118,level=0]{58 Building a superhero team} |
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\end{document} |
