Gibson Adam - Deep Learning
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Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learningespecially deep neural networksmake a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.
Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, youll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.
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by Josh Patterson and Adam Gibson
Copyright 2017 Josh Patterson and Adam Gibson. 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 Tim McGovern
- Production Editor: Nicholas Adams
- Copyeditor: Bob Russell, Octal Publishing, Inc.
- Proofreader: Christina Edwards
- Indexer: Judy McConville
- Interior Designer: David Futato
- Cover Designer: Karen Montgomery
- Illustrator: Rebecca Demarest
- August 2017: First Edition
- 2017-07-27: First Release
See http://oreilly.com/catalog/errata.csp?isbn=9781491914250 for release details.
The OReilly logo is a registered trademark of OReilly Media, Inc. 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.
978-1-491-91425-0
[M]
For my sons Ethan, Griffin, and Dane: Go forth, be persistent, be bold.
J. Patterson
The first four chapters of this book are focused on enough theory and fundamentals to give you, the practitioner, a working foundation for the rest of the book. The last five chapters then work from these concepts to lead you through a series of practical paths in deep learning using DL4J:
- Building deep networks
- Advanced tuning techniques
- Vectorization for different data types
- Running deep learning workflows on Spark
We use the names DL4J and Deeplearning4j interchangeably in this book. Both terms refer to the suite of tools in the Deeplearning4j library.
We designed the book in this manner because we felt there was a need for a book covering enough theory while being practical enough to build production-class deep learning workflows. We feel that this hybrid approach to the books coverage fits this space well.
is a review of machine learning concepts in general as well as deep learning in particular, to bring any reader up to speed on the basics needed to understand the rest of the book. We added this chapter because many beginners can use a refresher or primer on these concepts and we wanted to make the project accessible to the largest audience possible.
then introduces the four major architectures of deep networks and provides you with the foundation for the rest of the book.
In concludes the main body of the book with a review on how to use DL4J natively on Spark and Hadoop and illustrates three real examples that you can run on your own Spark clusters.
The book has many Appendix chapters for topics that were relevant yet didnt fit directly in the main chapters. Topics include:
- Artificial Intelligence
- Using Maven with DL4J projects
- Working with GPUs
- Using the ND4J API
- and more
Today, the term data science has no clean definition and often is used in many different ways. The world of data science and artificial intelligence (AI) is as broad and hazy as any terms in computer science today. This is largely because the world of machine learning has become entangled in nearly all disciplines.
This widespread entanglement has historical parallels to when the World Wide Web (90s) wove HTML into every discipline and brought many new people into the land of technology. In the same way, all typesengineers, statisticians, analysts, artistsare entering the machine learning fray every day. With this book, our goal is to democratize deep learning (and machine learning) and bring it to the broadest audience possible.
If you find the topic interesting and are reading this prefaceyou are the practitioner, and this book is for you.
As opposed to starting out with toy examples and building around those, we chose to start the book with a series of fundamentals to take you on a full journey through deep learning.
We feel that too many books leave out core topics that the enterprise practitioner often needs for a quick review. Based on our machine learning experiences in the field, we decided to lead-off with the materials that entry-level practitioners often need to brush up on to better support their deep learning projects.
You might want to skip Chapters and get right to the deep learning fundamentals. However, we expect that you will appreciate having the material up front so that you can have a smooth glide path into the more difficult topics in deep learning that build on these principles. In the following sections, we suggest some reading strategies for different backgrounds.
We split this category into two subgroups:
- Practicing data scientist
- Java engineer
This because youll probably be ready to jump into the fundamentals of deep networks.
Java should also be of keen interest to you because integration code for model scoring will typically touch ND4Js API directly.
Some of our reviewers were executives of large Fortune 500 companies and appreciated the content from the perspective of getting a better grasp on what is happening in deep learning. One executive commented that it had been a minute since college, and to reacclimate yourself to some terminology. You might want to skip the chapters that are heavy on APIs and examples, however.
If youre an academic, you likely will want to skip Chapters because graduate school will have already covered these topics. The chapters on tuning neural networks in general and then architecture-specific tuning will be of keen interest to you because this information is based on research and transcends any specific deep learning implementation. The coverage of ND4J will also be of interest to you if you prefer to do high-performance linear algebra on the Java Virtual Machine (JVM).
The following typographical conventions are used in this book:
ItalicIndicates new terms, URLs, email addresses, filenames, and file extensions.
Constant widthUsed 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. Also used for module and package names, and to show commands or other text that should be typed literally by the user and the output of commands.
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