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The Resource MATLAB deep learning : with machine learning, neural networks and artificial intelligence, Phil Kim

MATLAB deep learning : with machine learning, neural networks and artificial intelligence, Phil Kim

Label
MATLAB deep learning : with machine learning, neural networks and artificial intelligence
Title
MATLAB deep learning
Title remainder
with machine learning, neural networks and artificial intelligence
Statement of responsibility
Phil Kim
Creator
Author
Subject
Genre
Language
eng
Summary
Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolutional neural networks. In a blend of fundamentals and applications, MATLAB Deep Learning employs MATLAB as the underlying programming language and tool for the examples and case studies in this book. With this book, you'll be able to tackle some of today's real world big data, smart bots, and other complex data problems. You'll see how deep learning is a complex and more intelligent aspect of machine learning for modern smart data analysis and usage
Member of
Cataloging source
N$T
http://library.link/vocab/creatorName
Kim, Phil
Dewey number
511/.8
Index
index present
LC call number
TA345.5.M42
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/subjectName
  • Machine learning
  • Neural networks (Computer science)
  • Computer Science
  • Big Data
  • Artificial Intelligence (incl. Robotics)
  • Mathematical Logic and Formal Languages
  • Programming Languages, Compilers, Interpreters
  • Programming Techniques
  • Artificial intelligence
  • Mathematical theory of computation
  • Programming & scripting languages: general
  • Computer programming / software development
  • Databases
  • MATHEMATICS
  • COMPUTERS
  • Machine learning
  • Neural networks (Computer science)
Label
MATLAB deep learning : with machine learning, neural networks and artificial intelligence, Phil Kim
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learning?; Challenges with Machine Learning; Overfitting; Confronting Overfitting; Types of Machine Learning; Classification and Regression; Summary; Chapter 2: Neural Network; Nodes of a Neural Network; Layers of Neural Network; Supervised Learning of a Neural Network; Training of a Single-Layer Neural Network: Delta Rule; Generalized Delta Rule; SGD, Batch, and Mini Batch; Stochastic Gradient Descent; Batch; Mini Batch
  • Example: Delta RuleImplementation of the SGD Method; Implementation of the Batch Method; Comparison of the SGD and the Batch; Limitations of Single-Layer Neural Networks; Summary; Chapter 3: Training of Multi-Layer Neural Network; Back-Propagation Algorithm; Example: Back-Propagation; XOR Problem; Momentum; Cost Function and Learning Rule; Example: Cross Entropy Function; Cross Entropy Function; Comparison of Cost Functions; Summary; Chapter 4: Neural Network and Classification; Binary Classification; Multiclass Classification; Example: Multiclass Classification; Summary
  • Chapter 5: Deep LearningImprovement of the Deep Neural Network; Vanishing Gradient; Overfitting; Computational Load; Example: ReLU and Dropout; ReLU Function; Dropout; Summary; Chapter 6: Convolutional Neural Network; Architecture of ConvNet; Convolution Layer; Pooling Layer; Example: MNIST; Summary; Index
Control code
990267808
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781484228449
Lccn
2017944429
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-1-4842-2845-6
  • 9781484228449
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000898
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)990267808
Label
MATLAB deep learning : with machine learning, neural networks and artificial intelligence, Phil Kim
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learning?; Challenges with Machine Learning; Overfitting; Confronting Overfitting; Types of Machine Learning; Classification and Regression; Summary; Chapter 2: Neural Network; Nodes of a Neural Network; Layers of Neural Network; Supervised Learning of a Neural Network; Training of a Single-Layer Neural Network: Delta Rule; Generalized Delta Rule; SGD, Batch, and Mini Batch; Stochastic Gradient Descent; Batch; Mini Batch
  • Example: Delta RuleImplementation of the SGD Method; Implementation of the Batch Method; Comparison of the SGD and the Batch; Limitations of Single-Layer Neural Networks; Summary; Chapter 3: Training of Multi-Layer Neural Network; Back-Propagation Algorithm; Example: Back-Propagation; XOR Problem; Momentum; Cost Function and Learning Rule; Example: Cross Entropy Function; Cross Entropy Function; Comparison of Cost Functions; Summary; Chapter 4: Neural Network and Classification; Binary Classification; Multiclass Classification; Example: Multiclass Classification; Summary
  • Chapter 5: Deep LearningImprovement of the Deep Neural Network; Vanishing Gradient; Overfitting; Computational Load; Example: ReLU and Dropout; ReLU Function; Dropout; Summary; Chapter 6: Convolutional Neural Network; Architecture of ConvNet; Convolution Layer; Pooling Layer; Example: MNIST; Summary; Index
Control code
990267808
Dimensions
unknown
Extent
1 online resource
File format
unknown
Form of item
online
Isbn
9781484228449
Lccn
2017944429
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
  • 10.1007/978-1-4842-2845-6
  • 9781484228449
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000898
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)990267808

Library Locations

    • Ellis LibraryBorrow it
      1020 Lowry Street, Columbia, MO, 65201, US
      38.944491 -92.326012
    • Engineering Library & Technology CommonsBorrow it
      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
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