MATLAB Deep Learning.pdf

【实例简介】

开始使用matlab进行深入学习，使用ai进行深入入门。在这本书中，你从机器学习基础开始，然后转向神经网络，深入学习，然后卷积神经网络。在基础知识和应用程序的混合中，Matlab Deep Learning使用Matlab作为本书示例和案例研究的基础编程语言和工具。              有了这本书，您将能够解决当今现实世界中的一些大数据、智能机器人和其他复杂数据问题。您将看到深入学习对于现代智能数据分析和使用来说是机器学习的一个复杂和更智能的方面。
MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence
Phil Kim
Seoul, Soul-t'ukpyolsi, Korea(Republic of)
ISBN-13(pbk:978-1-48422844-9
ISBN-13( electronic:978-1-4842-2845-6
DOI10.1007/978-1-4842-2845-6
Library of Congress Control Number: 2017944429
Copyright o 2017 by Phil Kim
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Contents at a glance
About the author
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About the technical reviewer
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Acknowledgments,…
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Introduction
XV
Chapter 1: Machine Learning
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Chapter 2: Neural Network.m
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Chapter 3: Training of Multi-Layer Neural Network
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Chapter 4: Neural Network and classification.
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Chapter5: Deep Learning…,,…,,,,,,,,,103
Chapter 6: Convolutional Neural Network
121
Index
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149

Contents
About the author
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About the technical reviewer
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XI
Acknowledgments,,,,…,…,,,,,算,mxii
Introduction
XV
Chapter 1: Machine Learning
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What Is Machine Learning?
2
Challenges with Machine Learning……4
0 verfitting……
6
Confronting Overfitting.
10
Types of Machine Learning
12
Classification and Regression............................ 14
Summary
17
Chapter 2: Neural Network.at
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Nodes of a neural network
Layers of Neural Network
Supervised Learning of a Neural Network
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0279
Training of a single-layer Neural Network: Delta rule
Generalized Delta rule
32

CONTENTS
SGD, Batch and mini batch mm 34
Stochastic gradient descent
34
Batch….35
Mini batch
36
Example: Delta Rule
37
Implementation of the SGD Method
38
Implementation of the Batch Method
Comparison of the SGd and the batch
Limitations of single-Layer Neural Networks
35
Summary
.50
cHapter 3: Training of Multi-Layer Neural Network eamamanman 53
Back-Propagation Algorithm
54
Example: Back-Propagation
60
XOR Problem
Momentum
Cost Function and Learning Rule .
EXample: Cross Entropy function....,,,…,…,…………73
Cross Entropy Function
74
Comparison of Cost Functions
76
Summary…,,
Chapter 4: Neural Network and classification mmmmmmmmmm. 81
Binary classification
81
Multiclass classification
,86
Example: Multiclass Classification
93
Summary
102

CONTENtS
Chapter 5: Deep Learning
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103
Improvement of the Deep Neural Network.mmmmmmammmmmmmmn 105
Vanishing gradient.…
0 befitting…
107
Computational Load
EXample: Relu and dropout
109
RelU function m110
Dropout
114
Summary…,…,
120
Chapter6: Convolutional neural network,,…,……,121
Architecture of convNet mmmm. 121
Convolution Layer
124
Pooling Layer.
130
EXample: MNIST
131
Summary
147
Index…u149

About the author
Phil Kim, PhD is an experienced MAtlAB programmer and user. He also works
with algorithms of large datasets drawn from Al, and Machine Learning. He
has worked at the Korea Aerospace Research Institute as a Senior researcher
There, his main task was to develop autonomous flight algorithms and onboard
software for unmanned aerial vehicles. he developed an onscreen keyboard
program named"Clickey"during his period in the PhD program, which served
as a bridge to bring him to his current assignment as a Senior Research Officer at
the National rehabilitation research institute of Korea

About the technical
Reviewer
Jonah lissner is a research scientist advancing phd and dsc programs
scholarships, applied projects, and academic journal publications in theoretical
physics, power engineering, complex systems, metamaterials, geophysics,
and computation theory. He has strong cognitive ability in empiricism and
scientific reason for the purpose of hypothesis building theory learning, and
mathematical and axiomatic modeling and testing for abstract problem solving
His dissertations, research publications and projects, CV, journals, blog, novels,
andsystemarelistedathttp://lissneRresearch.weebly.com

Acknowledgments
Although i assume that the acknowledgements of most books are not relevant
to readers, I would like to offer some words of appreciation, as the following
people are very special to me. First, I am deeply grateful to those I studied
DeepLearningwithattheModulabs(www.modulabs.co.kr).Iowethemfor
teaching me most of what I know about Deep Learning. In addition, I offer my
heartfelt thanks to director s Kim of modulabs who allowed me to work in such
a wonderful place from spring to summer. I was able to finish the most of this
book at modulars
I also thank president Jeon from Bogonet, Dr H You, Dr Y.S. Kang, and
Mr..H Lee from KaRl, director S. Kim from Modulabs, and mr W. Lee and
Mr S Hwang from J. MARPLE. They devoted their time and efforts to reading and
revising the draft of this book. Although they gave me a hard time throughout the
revision process, I finished it without regret
Lastly, my deepest thanks and love to my wife, who is the best woman I have
ever met, and children, who never get bored of me and share precious memories
with me

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