Gerard Charlie - Practical Machine Learning In JavaScript. TensorFlow.js For Web Developers.pdf

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Practical Machine
Learning in
JavaScript
TensorFlow.js for Web Developers
—
Charlie Gerard
Practical Machine
Learning in JavaScript
TensorFlow.js for
Web Developers
Charlie Gerard
Practical Machine Learning in JavaScript: TensorFlow.js for Web
Developers
Charlie Gerard
Les Clayes sous bois, France
ISBN-13 (pbk): 978-1-4842-6417-1
https://doi.org/10.1007/978-1-4842-6418-8
ISBN-13 (electronic): 978-1-4842-6418-8
Copyright © 2021 by Charlie Gerard
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While the advice and information in this book are believed to be true and accurate at the date of
publication, neither the authors nor the editors nor the publisher can accept any legal
responsibility for any errors or omissions that may be made. The publisher makes no warranty,
express or implied, with respect to the material contained herein.
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source-code.
Printed on acid-free paper
To Joel, Jack and Daisy, just because. To me, for pushing
through a very tough year and still doing my
best writing this book.
Table of Contents
About the Author �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½ix
About the Technical Reviewer �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½xi
Acknowledgments �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½xiii
Introduction �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½xv
Chapter 1: The basics of machine learning �½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½�½1
1.1 What is machine learning?..............................................................................1
1.2 Types of machine learning ..............................................................................8
1.2.1 Supervised learning ...............................................................................9
1.2.2 Unsupervised learning..........................................................................10
1.2.3 Reinforcement learning ........................................................................12
1.2.4 Semi-supervised learning ....................................................................13
1.3 Algorithms .....................................................................................................14
1.3.1 Naive Bayes ..........................................................................................14
1.3.2 K-nearest neighbors .............................................................................15
1.3.3 Convolutional neural networks .............................................................16
1.4 Applications...................................................................................................18
1.4.1 Healthcare ............................................................................................18
1.4.2 Home automation .................................................................................20
1.4.3 Social good ...........................................................................................21
1.4.4 Art .........................................................................................................23
1.5 Summary.......................................................................................................24
v

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