没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
Nikhil Buduma
Fundamentals of
Deep
Learning
DESIGNING NEXT-GENERATION
ARTIFICIAL INTELLIGENCE ALGORITHMS
Compliments of
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
Fundamentals of Deep Learning
Designing Next Generation
Articial Intelligence Algorithms
Boston Farnham Sebastopol
Tokyo
Beijing Boston Farnham Sebastopol
Tokyo
Beijing
978-1-491-92561-4
[LSI]
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.
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
剩余74页未读,继续阅读
资源评论
ytian6ncsu
- 粉丝: 1
- 资源: 3
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功