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Hands-On Machine Learning with Scikit-Learn, Keras,TensorFlow
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Aurélien Géron
Hands-on
Machine Learning
with Scikit-Learn,
Keras & TensorFlow
Concepts, Tools, and Techniques
to Build Intelligent Systems
TM
2nd Edition
Updated for
TensorFlow 2
Aurélien Géron
Hands-On Machine Learning with
Scikit-Learn, Keras, and
TensorFlow
Concepts, Tools, and Techniques to
Build Intelligent Systems
SECOND EDITION
Boston Farnham Sebastopol
Tokyo
Beijing Boston Farnham Sebastopol
Tokyo
Beijing
K
5
0
0
1
978-1-492-03264-9
[TI]
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
by Aurélien Géron
Copyright © 2019 Kiwisoft S.A.S. All rights reserved.
Printed in Canada.
Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472.
O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are
also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional
sales department: 800-998-9938 or corporate@oreilly.com.
Editors: Rachel Roumeliotis and Nicole Tache
Production Editor: Kristen Brown
Copyeditor: Amanda Kersey
Proofreader: Rachel Head
Indexer: Judith McConville
Interior Designer: David Futato
Cover Designer: Karen Montgomery
Illustrator: Rebecca Demarest
September 2019: Second Edition
Revision History for the Second Edition
2019-09-05: First Release
2019-10-11: Second Release
2019-11-22: Third Release
See http://oreilly.com/catalog/errata.csp?isbn=9781492032649 for release details.
The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Hands-On Machine Learning with
Scikit-Learn, Keras, and TensorFlow, the cover image, and related trade dress are trademarks of O’Reilly
Media, Inc.
The views expressed in this work are those of the author, and do not represent the publisher’s views.
While the publisher and the author have used good faith efforts to ensure that the information and
instructions contained in this work are accurate, the publisher and the author disclaim all responsibility
for errors or omissions, including without limitation responsibility for damages resulting from the use of
or reliance on this work. Use of the information and instructions contained in this work is at your own
risk. If any code samples or other technology this work contains or describes is subject to open source
licenses or the intellectual property rights of others, it is your responsibility to ensure that your use
thereof complies with such licenses and/or rights.
Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
Part I. The Fundamentals of Machine Learning
1.
The Machine Learning Landscape. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
What Is Machine Learning? 2
Why Use Machine Learning? 2
Examples of Applications 5
Types of Machine Learning Systems 7
Supervised/Unsupervised Learning 7
Batch and Online Learning 14
Instance-Based Versus Model-Based Learning 17
Main Challenges of Machine Learning 23
Insufficient Quantity of Training Data 23
Nonrepresentative Training Data 25
Poor-Quality Data 26
Irrelevant Features 27
Overfitting the Training Data 27
Underfitting the Training Data 29
Stepping Back 30
Testing and Validating 30
Hyperparameter Tuning and Model Selection 31
Data Mismatch 32
Exercises 33
2.
End-to-End Machine Learning Project. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Working with Real Data 35
iii
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