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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

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

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Printed on acid-free paper

iii

Contents at a Glance

About the Author �� xiii

About the Technical Reviewer �� xv

Acknowledgments �� xvii

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

Contents

About the Author �� xiii

About the Technical Reviewer �� xv

Acknowledgments �� xvii

Introduction �� xix

■Chapter 1: Introduction to Deep Learning �� 1

Deep Learning Models �� 3

Single Layer Perceptron Model (SLP) ��3

Multilayer Perceptron Model (MLP) ��4

Convolutional Neural Networks (CNNs) ��5

Recurrent Neural Networks (RNNs) ��5

Restricted Boltzmann Machines (RBMs) ��6

Deep Belief Networks (DBNs) ��6

Introduction to Deep Learning Using R

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