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深度学习基础(Fundamentals of Deep Learning)

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  • 日期: 2018-11-07
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深度学习基础(Fundamentals of Deep Learning)

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C o m plim e nts of Fundamentals of Deep Learning DESIGNING NEXTGENERATION ARTIFICIAL INTELLIGENCE ALGORITHMS Nikhil Buduma Fundamentals of Deep Learning Designing Next Generation Artificial Intelligence Algorithms This Preview Edition of Fundamentals of Deep Learning Chapters 13 is a work in progress The final book is expected to release on oreillycom and through other retailers in December 2016 Nikhil Buduma Beijing Beijing Boston Boston Farnham Sebastopol Farnham Sebastopol Tokyo Tokyo......

C o m plim e nts of Fundamentals of Deep Learning DESIGNING NEXT-GENERATION ARTIFICIAL INTELLIGENCE ALGORITHMS Nikhil Buduma Fundamentals of Deep Learning Designing Next Generation Artificial Intelligence Algorithms This Preview Edition of Fundamentals of Deep Learning, Chapters 1–3, is a work in progress. The final book is expected to release on oreilly.com and through other retailers in December, 2016. Nikhil Buduma Beijing Beijing Boston Boston Farnham Sebastopol Farnham Sebastopol Tokyo Tokyo Fundamentals of Deep Learning by Nikhil Buduma Copyright © 2015 Nikhil Buduma. All rights reserved. Printed in the United States of America. 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://safaribooksonline.com). For more information, contact our corporate/ institutional sales department: 800-998-9938 or corporate@oreilly.com. Editors: Mike Loukides and Shannon Cutt Production Editor: Copyeditor: Proofreader: Indexer: Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Panzer November 2015: First Edition Revision History for the First Edition 2015-06-12 First Early Release 2015-07-23 Second Early Release See http://oreilly.com/catalog/errata.csp?isbn=9781491925614 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Fundamentals of Deep Learning, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. 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. 978-1-491-92561-4 [LSI] Table of Contents 1. The Neural Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Building Intelligent Machines 5 The Limits of Traditional Computer Programs 6 The Mechanics of Machine Learning 7 The Neuron 11 Expressing Linear Perceptrons as Neurons 13 Feed-forward Neural Networks 14 Linear Neurons and their Limitations 17 Sigmoid, Tanh, and ReLU Neurons 17 Softmax Output Layers 19 Looking Forward 20 2. Training Feed-Forward Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 The Cafeteria Problem 21 Gradient Descent 23 The Delta Rule and Learning Rates 25 Gradient Descent with Sigmoidal Neurons 27 The Backpropagation Algorithm 29 Stochastic and Mini-Batch Gradient Descent 32 Test Sets, Validation Sets, and Overfitting 34 Preventing Overfitting in Deep Neural Networks 41 Summary 45 3. Implementing Neural Networks in TensorFlow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 What is TensorFlow? 47 How Does TensorFlow Compare to Alternatives? 48 Installing TensorFlow 49 Creating and Manipulating TensorFlow Variables 51 iii
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