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Introduction to Deep Learning Using R 原版PDF by Beysolow II
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It is assumed that all readers have at least an elementary understanding of statistical or computer programming, specifically with respect to the R programming language. Those who do not will find it much more difficult to follow the sections of this book which give examples of code to use, and it is suggested that they return to this text upon gaining that information.
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Introduction
to Deep Learning
Using R
A Step-by-Step Guide to
Learning and Implementing
Deep Learning Models Using R
—
Taweh Beysolow II

Introduction to Deep
Learning Using R
A Step-by-Step Guide to
Learning and Implementing
Deep Learning Models Using R
Taweh Beysolow II

Introduction to Deep Learning Using R
Taweh Beysolow II
San Francisco, California, USA
ISBN-13 (pbk): 978-1-4842-2733-6 ISBN-13 (electronic): 978-1-4842-2734-3
DOI 10.1007/978-1-4842-2734-3
Library of Congress Control Number: 2017947908
Copyright © 2017 by Taweh Beysolow II
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole
or part of the material is concerned, specifically the rights of translation, reprinting, reuse of
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The use in this publication of trade names, trademarks, service marks, and similar terms, even if
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they are subject to proprietary rights.
While the advice and information in this book are believed to be true and accurate at the
date of publication, neither the authors nor the editors nor the publisher can accept any legal
responsibility for any errors or omissions that may be made. The publisher makes no warranty,
express or implied, with respect to the material contained herein.
Cover image designed by Freepik
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Printed on acid-free paper

iii
Contents at a Glance
Introduction ������������������������������������������������������������������������������������ xix
■Chapter 1: Introduction to Deep Learning �������������������������������������� 1
■Chapter 2: Mathematical Review ������������������������������������������������� 11
■Chapter 3: A Review of Optimization and Machine Learning ������� 45
■Chapter 4: Single and Multilayer Perceptron Models ������������������� 89
■Chapter 5: Convolutional Neural Networks (CNNs) ��������������������� 101
■Chapter 6: Recurrent Neural Networks (RNNs)��������������������������� 113
■ Chapter 7: Autoencoders, Restricted Boltzmann Machines,
and Deep Belief Networks ���������������������������������������������������������� 125
■Chapter 8: Experimental Design and Heuristics ������������������������� 137
■Chapter 9: Hardware and Software Suggestions ������������������������ 167
■Chapter 10: Machine Learning Example Problems ��������������������� 171
■Chapter 11: Deep Learning and Other Example Problems ���������� 195
■Chapter 12: Closing Statements ������������������������������������������������� 219
Index ���������������������������������������������������������������������������������������������� 221

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