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
Resource Information
The item MATLAB deep learning : with machine learning, neural networks and artificial intelligence, Phil Kim represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.This item is available to borrow from 2 library branches.
Resource Information
The item MATLAB deep learning : with machine learning, neural networks and artificial intelligence, Phil Kim represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Missouri Libraries.
This item is available to borrow from 2 library branches.
 Summary
 Get started with MATLAB for deep learning and AI with this indepth 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
 Language
 eng
 Extent
 1 online resource
 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 SingleLayer 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 SingleLayer Neural Networks; Summary; Chapter 3: Training of MultiLayer Neural Network; BackPropagation Algorithm; Example: BackPropagation; 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
 Isbn
 9781484228449
 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
 Subject

 Artificial intelligence
 Big Data
 COMPUTERS  Neural Networks
 Computer Science
 Computer programming / software development
 Databases
 Electronic books
 MATHEMATICS  General
 MATLAB
 MATLAB
 Machine learning
 Mathematical Logic and Formal Languages
 Mathematical theory of computation
 Neural networks (Computer science)
 Neural networks (Computer science)
 Programming & scripting languages: general
 Programming Languages, Compilers, Interpreters
 Programming Techniques
 Machine learning
 Artificial Intelligence (incl. Robotics)
 Language
 eng
 Summary
 Get started with MATLAB for deep learning and AI with this indepth 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
 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
 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 SingleLayer 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 SingleLayer Neural Networks; Summary; Chapter 3: Training of MultiLayer Neural Network; BackPropagation Algorithm; Example: BackPropagation; 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

 10.1007/9781484228456
 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
 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 SingleLayer 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 SingleLayer Neural Networks; Summary; Chapter 3: Training of MultiLayer Neural Network; BackPropagation Algorithm; Example: BackPropagation; 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

 10.1007/9781484228456
 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
Subject
 Artificial intelligence
 Big Data
 COMPUTERS  Neural Networks
 Computer Science
 Computer programming / software development
 Databases
 Electronic books
 MATHEMATICS  General
 MATLAB
 MATLAB
 Machine learning
 Mathematical Logic and Formal Languages
 Mathematical theory of computation
 Neural networks (Computer science)
 Neural networks (Computer science)
 Programming & scripting languages: general
 Programming Languages, Compilers, Interpreters
 Programming Techniques
 Machine learning
 Artificial Intelligence (incl. Robotics)
Genre
Member of
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