Please use this identifier to cite or link to this item:
http://localhost/handle/Hannan/415
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Phil, ; author ; | en_US |
| dc.date.accessioned | 2013 | en_US |
| dc.date.accessioned | 2020-04-28T08:53:59Z | - |
| dc.date.available | 2020-04-28T08:53:59Z | - |
| dc.date.issued | 2017 | en_US |
| dc.identifier.isbn | 9781484228456 ; | en_US |
| dc.identifier.isbn | 1484228456 ; | en_US |
| dc.identifier.isbn | 1484228448 ; | en_US |
| dc.identifier.isbn | 9781484228449 ; | en_US |
| dc.identifier.uri | http://localhost/handle/Hannan/415 | - |
| dc.description | en_US | |
| dc.description | Available to OhioLINK libraries ; | en_US |
| dc.description | en_US | |
| dc.description | en_US | |
| dc.description | Ohio Library and Information Network ; | en_US |
| dc.description | en_US | |
| dc.description | Printed edition: ; 9781484228449 ; | en_US |
| dc.description | en_US | |
| dc.description.abstract | 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 ; | en_US |
| dc.description.statementofresponsibility | Phil Kim | en_US |
| dc.description.tableofcontents | At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learninge; 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 ; | en_US |
| dc.description.tableofcontents | 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 ; | en_US |
| dc.description.tableofcontents | 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 ; | en_US |
| dc.format.extent | 1 online resource ; | en_US |
| dc.format.extent | Includes index ; | en_US |
| dc.format.extent | Includes bibliographical references ; | en_US |
| dc.publisher | Apress, | en_US |
| dc.relation.haspart | 9781484228456.pdf | en_US |
| dc.subject | MATLAB ; | en_US |
| dc.subject | Machine learning ; | en_US |
| dc.subject | Neural networks (Computer science) ; | en_US |
| dc.subject | Matlab (Computer Program) ; | en_US |
| dc.subject | Computers ; Mathematical & Statistical Software ; | en_US |
| dc.title | MATLAB deep learning : | en_US |
| dc.title.alternative | with machine learning, neural networks and artificial intelligence / | en_US |
| dc.type | Book | en_US |
| dc.publisher.place | [New York, NY] : | en_US |
| dc.classification.lc | TA345.5.M42 ; | en_US |
| dc.classification.dc | 511/.8 ; 23 ; | en_US |
| Appears in Collections: | مهندسی مدیریت ساخت | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 9781484228456.pdf | 3.73 MB | Adobe PDF |  Preview File |
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Phil, ; author ; | en_US |
| dc.date.accessioned | 2013 | en_US |
| dc.date.accessioned | 2020-04-28T08:53:59Z | - |
| dc.date.available | 2020-04-28T08:53:59Z | - |
| dc.date.issued | 2017 | en_US |
| dc.identifier.isbn | 9781484228456 ; | en_US |
| dc.identifier.isbn | 1484228456 ; | en_US |
| dc.identifier.isbn | 1484228448 ; | en_US |
| dc.identifier.isbn | 9781484228449 ; | en_US |
| dc.identifier.uri | http://localhost/handle/Hannan/415 | - |
| dc.description | en_US | |
| dc.description | Available to OhioLINK libraries ; | en_US |
| dc.description | en_US | |
| dc.description | en_US | |
| dc.description | Ohio Library and Information Network ; | en_US |
| dc.description | en_US | |
| dc.description | Printed edition: ; 9781484228449 ; | en_US |
| dc.description | en_US | |
| dc.description.abstract | 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 ; | en_US |
| dc.description.statementofresponsibility | Phil Kim | en_US |
| dc.description.tableofcontents | At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learninge; 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 ; | en_US |
| dc.description.tableofcontents | 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 ; | en_US |
| dc.description.tableofcontents | 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 ; | en_US |
| dc.format.extent | 1 online resource ; | en_US |
| dc.format.extent | Includes index ; | en_US |
| dc.format.extent | Includes bibliographical references ; | en_US |
| dc.publisher | Apress, | en_US |
| dc.relation.haspart | 9781484228456.pdf | en_US |
| dc.subject | MATLAB ; | en_US |
| dc.subject | Machine learning ; | en_US |
| dc.subject | Neural networks (Computer science) ; | en_US |
| dc.subject | Matlab (Computer Program) ; | en_US |
| dc.subject | Computers ; Mathematical & Statistical Software ; | en_US |
| dc.title | MATLAB deep learning : | en_US |
| dc.title.alternative | with machine learning, neural networks and artificial intelligence / | en_US |
| dc.type | Book | en_US |
| dc.publisher.place | [New York, NY] : | en_US |
| dc.classification.lc | TA345.5.M42 ; | en_US |
| dc.classification.dc | 511/.8 ; 23 ; | en_US |
| Appears in Collections: | مهندسی مدیریت ساخت | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 9781484228456.pdf | 3.73 MB | Adobe PDF | Preview File |
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kim, Phil, ; author ; | en_US |
| dc.date.accessioned | 2013 | en_US |
| dc.date.accessioned | 2020-04-28T08:53:59Z | - |
| dc.date.available | 2020-04-28T08:53:59Z | - |
| dc.date.issued | 2017 | en_US |
| dc.identifier.isbn | 9781484228456 ; | en_US |
| dc.identifier.isbn | 1484228456 ; | en_US |
| dc.identifier.isbn | 1484228448 ; | en_US |
| dc.identifier.isbn | 9781484228449 ; | en_US |
| dc.identifier.uri | http://localhost/handle/Hannan/415 | - |
| dc.description | en_US | |
| dc.description | Available to OhioLINK libraries ; | en_US |
| dc.description | en_US | |
| dc.description | en_US | |
| dc.description | Ohio Library and Information Network ; | en_US |
| dc.description | en_US | |
| dc.description | Printed edition: ; 9781484228449 ; | en_US |
| dc.description | en_US | |
| dc.description.abstract | 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 ; | en_US |
| dc.description.statementofresponsibility | Phil Kim | en_US |
| dc.description.tableofcontents | At a Glance; Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Machine Learning; What Is Machine Learninge; 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 ; | en_US |
| dc.description.tableofcontents | 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 ; | en_US |
| dc.description.tableofcontents | 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 ; | en_US |
| dc.format.extent | 1 online resource ; | en_US |
| dc.format.extent | Includes index ; | en_US |
| dc.format.extent | Includes bibliographical references ; | en_US |
| dc.publisher | Apress, | en_US |
| dc.relation.haspart | 9781484228456.pdf | en_US |
| dc.subject | MATLAB ; | en_US |
| dc.subject | Machine learning ; | en_US |
| dc.subject | Neural networks (Computer science) ; | en_US |
| dc.subject | Matlab (Computer Program) ; | en_US |
| dc.subject | Computers ; Mathematical & Statistical Software ; | en_US |
| dc.title | MATLAB deep learning : | en_US |
| dc.title.alternative | with machine learning, neural networks and artificial intelligence / | en_US |
| dc.type | Book | en_US |
| dc.publisher.place | [New York, NY] : | en_US |
| dc.classification.lc | TA345.5.M42 ; | en_US |
| dc.classification.dc | 511/.8 ; 23 ; | en_US |
| Appears in Collections: | مهندسی مدیریت ساخت | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 9781484228456.pdf | 3.73 MB | Adobe PDF | Preview File |