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Delip Rao - Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning

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Delip Rao Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning
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From the PrefaceThis book aims to bring newcomers to natural language processing (NLP) and deep learning to a tasting table covering important topics in both areas. Both of these subject areas are growing exponentially. As it introduces both deep learning and NLP with an emphasis on implementation, this book occupies an important middle ground. While writing the book, we had to make difficult, and sometimes uncomfortable, choices on what material to leave out. For a beginner reader, we hope the book will provide a strong foundation in the basics and a glimpse of what is possible. Machine learning, and deep learning in particular, is an experiential discipline, as opposed to an intellectual science. The generous end-to-end code examples in each chapter invite you to partake in that experience.A note regarding the style of the book.We have intentionally avoided mathematics in most places, not because deep learning math is particularly difficult (it is not), but because it is a distraction in many situations from the main goal of this bookto empower the beginner learner.Likewise, in many cases, both in code and text, we have favored exposition over succinctness. Advanced readers and experienced programmers will likely see ways to tighten up the code and so on, but our choice was to be as explicit as possible so as to reach the broadest of the audience that we want to reach.

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Natural Language Processing with PyTorch

by Delip Rao and Brian McMahan

Copyright 2019 Delip Rao and Brian McMahan. 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 .

Acquisition Editor: Rachel Roumeliotis

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Copyeditor: Octal Publishing, LLC

Proofreader: Rachel Head

Indexer: Judy McConville

Interior Designer: David Futato

Cover Designer: Karen Montgomery

Illustrator: Rebecca Demarest

  • February 2019: First Edition
Revision History for the First Edition
  • 2019-01-16: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781491978238 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. Natural Language Processing with PyTorch, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

The views expressed in this work are those of the authors, and do not represent the publishers views. 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.

978-1-491-97823-8

[LSI]

Preface

This book aims to bring newcomers to natural language processing (NLP) and deep learning to a tasting table covering important topics in both areas. Both of these subject areas are growing exponentially. As it introduces both deep learning and NLP with an emphasis on implementation, this book occupies an important middle ground. While writing the book, we had to make difficult, and sometimes uncomfortable, choices on what material to leave out. For a beginner reader, we hope the book will provide a strong foundation in the basics and a glimpse of what is possible. Machine learning, and deep learning in particular, is an experiential discipline, as opposed to an intellectual science. The generous end-to-end code examples in each chapter invite you to partake in that experience.

When we began working on the book, we started with PyTorch 0.2. The examples were revised with each PyTorch update from 0.2 to 0.4.

A note regarding the style of the book. We have intentionally avoided mathematics in most places, not because deep learning math is particularly difficult (it is not), but because it is a distraction in many situations from the main goal of this bookto empower the beginner learner. Likewise, in many cases, both in code and text, we have favored exposition over succinctness. Advanced readers and experienced programmers will likely see ways to tighten up the code and so on, but our choice was to be as explicit as possible so as to reach the broadest of the audience that we want to reach.

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.

Tip

This element signifies a tip or suggestion.

Note

This element signifies a general note.

Warning

This element indicates a warning or caution.

Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://nlproc.info/PyTorchNLPBook/repo/.

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: Natural Language Processing with PyTorch by Delip Rao and Brian McMahan (OReilly). Copyright 2019, Delip Rao and Brian McMahan, 978-1-491-97823-8.

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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