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Nikhil Buduma - Fundamentals of Deep Learning

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Nikhil Buduma Fundamentals of Deep Learning
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Fundamentals of Deep Learning

by Nikhil Buduma and Nicholas Lacascio

Copyright 2017 Nikhil Buduma. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

  • Editors: Mike Loukides and Shannon Cutt
  • Production Editor: Shiny Kalapurakkel
  • Copyeditor: Sonia Saruba
  • Proofreader: Amanda Kersey
  • Indexer: Wendy Catalano
  • Interior Designer: David Futato
  • Cover Designer: Karen Montgomery
  • Illustrator: Rebecca Demarest
  • June 2017: First Edition
Revision History for the First Edition
  • 2017-05-25: First Release
  • 2017-07-07: Second Release

The OReilly logo is a registered trademark of OReilly Media, Inc. Fundamentals of Deep Learning, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors 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.

978-1-491-92561-4

[LSI]

Preface

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Google, Microsoft, and Facebook have taken notice and are actively growing in-house deep learning teams. For the rest of us, deep learning is still a pretty complex and difficult subject to grasp. Research papers are filled to the brim with jargon, and scattered online tutorials do little to help build a strong intuition for why and how deep learning practitioners approach problems. Our goal is to bridge this gap.

Prerequisites and Objectives

This booked is aimed an audience with a basic operating understanding of calculus, matrices, and Python programming. Approaching this material without this background is possible, but likely to be more challenging. Background in linear algebra may also be helpful in navigating certain sections of mathematical exposition.

By the end of the book, we hope that our readers will be left with an intuition for how to approach problems using deep learning, the historical context for modern deep learning approaches, and a familiarity with implementing deep learning algorithms using the TensorFlow open source library.

Conventions Used in This Book

The following typographical conventions are used in this book:

Italic

Indicates new terms, URLs, email addresses, filenames, and file extensions.

Constant width

Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords.

Constant width bold

Shows commands or other text that should be typed literally by the user.

Constant width italic

Shows text that should be replaced with user-supplied values or by values determined by context.

Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/darksigma/Fundamentals-of-Deep-Learning-Book.

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless youre reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from OReilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your products documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: Fundamentals of Deep Learning by Nikhil Buduma and Nicholas Locascio (OReilly). Copyright 2017 Nikhil Buduma and Nicholas Locascio, 978-1-491-92561-4.

If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .

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Acknowledgements

Wed like to thank several people who have been instrumental in the completion of this text. Wed like to start by acknowledging Mostafa Samir and Surya Bhupatiraju, who contributed heavily to the content of Chapters . We also appreciate the contributions of Mohamed (Hassan) Kane and Anish Athalye, who worked on early versions of the code examples in this books Github repository.

This book would not have been possible without the never-ending support and expertise of our editor, Shannon Cutt. Wed also like to appreciate the commentary provided by our reviewers, Isaac Hodes, David Andrzejewski, and Aaron Schumacher, who provided thoughtful, in-depth commentary on the original drafts of the text. Finally, we are thankful for all of the insight provided by our friends and family members, including Jeff Dean, Nithin Buduma, Venkat Buduma, and William, Jack, as we finalized the manuscript of the text.

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