The Resource TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh
TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh
Resource Information
The item TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Massey University Library, University of New Zealand.This item is available to borrow from 1 library branch.
Resource Information
The item TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Massey University Library, University of New Zealand.
This item is available to borrow from 1 library branch.
 Language
 eng
 Edition
 First edition
 Extent
 1 online resource
 Note
 Includes index
 Contents

 Cover; Copyright; Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; Oâ#x80;#x99;Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Introduction to Deep Learning; Machine Learning Eats Computer Science; Deep Learning Primitives; Fully Connected Layer; Convolutional Layer; Recurrent Neural Network Layers; Long ShortTerm Memory Cells; Deep Learning Architectures; LeNet; AlexNet; ResNet; Neural Captioning Model; Google Neural Machine Translation; OneShot Models; AlphaGo; Generative Adversarial Networks; Neural Turing Machines; Deep Learning Frameworks
 Limitations of TensorFlowReview; Chapter 2. Introduction to TensorFlow Primitives; Introducing Tensors; Scalars, Vectors, and Matrices; Matrix Mathematics; Tensors; Tensors in Physics; Mathematical Asides; Basic Computations in TensorFlow; Installing TensorFlow and Getting Started; Initializing Constant Tensors; Sampling Random Tensors; Tensor Addition and Scaling; Matrix Operations; Tensor Types; Tensor Shape Manipulations; Introduction to Broadcasting; Imperative and Declarative Programming; TensorFlow Graphs; TensorFlow Sessions; TensorFlow Variables; Review
 Chapter 3. Linear and Logistic Regression with TensorFlowMathematical Review; Functions and Differentiability; Loss Functions; Gradient Descent; Automatic Differentiation Systems; Learning with TensorFlow; Creating Toy Datasets; New TensorFlow Concepts; Training Linear and Logistic Models in TensorFlow; Linear Regression in TensorFlow; Logistic Regression in TensorFlow; Review; Chapter 4. Fully Connected Deep Networks; What Is a Fully Connected Deep Network?; â#x80;#x9C;Neuronsâ#x80;#x9D; in Fully Connected Networks; Learning Fully Connected Networks with Backpropagation; Universal Convergence Theorem
 Why Deep Networks?Training Fully Connected Neural Networks; Learnable Representations; Activations; Fully Connected Networks Memorize; Regularization; Training Fully Connected Networks; Implementation in TensorFlow; Installing DeepChem; Tox21 Dataset; Accepting Minibatches of Placeholders; Implementing a Hidden Layer; Adding Dropout to a Hidden Layer; Implementing Minibatching; Evaluating Model Accuracy; Using TensorBoard to Track Model Convergence; Review; Chapter 5. Hyperparameter Optimization; Model Evaluation and Hyperparameter Optimization; Metrics, Metrics, Metrics
 Binary Classification MetricsMulticlass Classification Metrics; Regression Metrics; Hyperparameter Optimization Algorithms; Setting Up a Baseline; Graduate Student Descent; Grid Search; Random Hyperparameter Search; Challenge for the Reader; Review; Chapter 6. Convolutional Neural Networks; Introduction to Convolutional Architectures; Local Receptive Fields; Convolutional Kernels; Pooling Layers; Constructing Convolutional Networks; Dilated Convolutions; Applications of Convolutional Networks; Object Detection and Localization; Image Segmentation; Graph Convolutions
 Isbn
 9781491980422
 Label
 TensorFlow for deep learning : from linear regression to reinforcement learning
 Title
 TensorFlow for deep learning
 Title remainder
 from linear regression to reinforcement learning
 Statement of responsibility
 Bharath Ramsundar and Reza Bosagh Zadeh
 Language
 eng
 Cataloging source
 N$T
 http://library.link/vocab/creatorName
 Ramsundar, Bharath
 Dewey number
 006.31
 Index
 index present
 Literary form
 non fiction
 Nature of contents
 dictionaries
 http://library.link/vocab/relatedWorkOrContributorName
 Zadeh, Reza Bosagh
 http://library.link/vocab/subjectName

 Machine learning
 Reinforcement learning
 Artificial intelligence
 Label
 TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh
 Note
 Includes index
 Antecedent source
 unknown
 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

 Cover; Copyright; Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; Oâ#x80;#x99;Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Introduction to Deep Learning; Machine Learning Eats Computer Science; Deep Learning Primitives; Fully Connected Layer; Convolutional Layer; Recurrent Neural Network Layers; Long ShortTerm Memory Cells; Deep Learning Architectures; LeNet; AlexNet; ResNet; Neural Captioning Model; Google Neural Machine Translation; OneShot Models; AlphaGo; Generative Adversarial Networks; Neural Turing Machines; Deep Learning Frameworks
 Limitations of TensorFlowReview; Chapter 2. Introduction to TensorFlow Primitives; Introducing Tensors; Scalars, Vectors, and Matrices; Matrix Mathematics; Tensors; Tensors in Physics; Mathematical Asides; Basic Computations in TensorFlow; Installing TensorFlow and Getting Started; Initializing Constant Tensors; Sampling Random Tensors; Tensor Addition and Scaling; Matrix Operations; Tensor Types; Tensor Shape Manipulations; Introduction to Broadcasting; Imperative and Declarative Programming; TensorFlow Graphs; TensorFlow Sessions; TensorFlow Variables; Review
 Chapter 3. Linear and Logistic Regression with TensorFlowMathematical Review; Functions and Differentiability; Loss Functions; Gradient Descent; Automatic Differentiation Systems; Learning with TensorFlow; Creating Toy Datasets; New TensorFlow Concepts; Training Linear and Logistic Models in TensorFlow; Linear Regression in TensorFlow; Logistic Regression in TensorFlow; Review; Chapter 4. Fully Connected Deep Networks; What Is a Fully Connected Deep Network?; â#x80;#x9C;Neuronsâ#x80;#x9D; in Fully Connected Networks; Learning Fully Connected Networks with Backpropagation; Universal Convergence Theorem
 Why Deep Networks?Training Fully Connected Neural Networks; Learnable Representations; Activations; Fully Connected Networks Memorize; Regularization; Training Fully Connected Networks; Implementation in TensorFlow; Installing DeepChem; Tox21 Dataset; Accepting Minibatches of Placeholders; Implementing a Hidden Layer; Adding Dropout to a Hidden Layer; Implementing Minibatching; Evaluating Model Accuracy; Using TensorBoard to Track Model Convergence; Review; Chapter 5. Hyperparameter Optimization; Model Evaluation and Hyperparameter Optimization; Metrics, Metrics, Metrics
 Binary Classification MetricsMulticlass Classification Metrics; Regression Metrics; Hyperparameter Optimization Algorithms; Setting Up a Baseline; Graduate Student Descent; Grid Search; Random Hyperparameter Search; Challenge for the Reader; Review; Chapter 6. Convolutional Neural Networks; Introduction to Convolutional Architectures; Local Receptive Fields; Convolutional Kernels; Pooling Layers; Constructing Convolutional Networks; Dilated Convolutions; Applications of Convolutional Networks; Object Detection and Localization; Image Segmentation; Graph Convolutions
 Control code
 on1027476936
 Dimensions
 unknown
 Edition
 First edition
 Extent
 1 online resource
 File format
 unknown
 Form of item
 online
 Isbn
 9781491980422
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Quality assurance targets
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)1027476936
 Label
 TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh
 Note
 Includes index
 Antecedent source
 unknown
 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

 Cover; Copyright; Table of Contents; Preface; Conventions Used in This Book; Using Code Examples; Oâ#x80;#x99;Reilly Safari; How to Contact Us; Acknowledgments; Chapter 1. Introduction to Deep Learning; Machine Learning Eats Computer Science; Deep Learning Primitives; Fully Connected Layer; Convolutional Layer; Recurrent Neural Network Layers; Long ShortTerm Memory Cells; Deep Learning Architectures; LeNet; AlexNet; ResNet; Neural Captioning Model; Google Neural Machine Translation; OneShot Models; AlphaGo; Generative Adversarial Networks; Neural Turing Machines; Deep Learning Frameworks
 Limitations of TensorFlowReview; Chapter 2. Introduction to TensorFlow Primitives; Introducing Tensors; Scalars, Vectors, and Matrices; Matrix Mathematics; Tensors; Tensors in Physics; Mathematical Asides; Basic Computations in TensorFlow; Installing TensorFlow and Getting Started; Initializing Constant Tensors; Sampling Random Tensors; Tensor Addition and Scaling; Matrix Operations; Tensor Types; Tensor Shape Manipulations; Introduction to Broadcasting; Imperative and Declarative Programming; TensorFlow Graphs; TensorFlow Sessions; TensorFlow Variables; Review
 Chapter 3. Linear and Logistic Regression with TensorFlowMathematical Review; Functions and Differentiability; Loss Functions; Gradient Descent; Automatic Differentiation Systems; Learning with TensorFlow; Creating Toy Datasets; New TensorFlow Concepts; Training Linear and Logistic Models in TensorFlow; Linear Regression in TensorFlow; Logistic Regression in TensorFlow; Review; Chapter 4. Fully Connected Deep Networks; What Is a Fully Connected Deep Network?; â#x80;#x9C;Neuronsâ#x80;#x9D; in Fully Connected Networks; Learning Fully Connected Networks with Backpropagation; Universal Convergence Theorem
 Why Deep Networks?Training Fully Connected Neural Networks; Learnable Representations; Activations; Fully Connected Networks Memorize; Regularization; Training Fully Connected Networks; Implementation in TensorFlow; Installing DeepChem; Tox21 Dataset; Accepting Minibatches of Placeholders; Implementing a Hidden Layer; Adding Dropout to a Hidden Layer; Implementing Minibatching; Evaluating Model Accuracy; Using TensorBoard to Track Model Convergence; Review; Chapter 5. Hyperparameter Optimization; Model Evaluation and Hyperparameter Optimization; Metrics, Metrics, Metrics
 Binary Classification MetricsMulticlass Classification Metrics; Regression Metrics; Hyperparameter Optimization Algorithms; Setting Up a Baseline; Graduate Student Descent; Grid Search; Random Hyperparameter Search; Challenge for the Reader; Review; Chapter 6. Convolutional Neural Networks; Introduction to Convolutional Architectures; Local Receptive Fields; Convolutional Kernels; Pooling Layers; Constructing Convolutional Networks; Dilated Convolutions; Applications of Convolutional Networks; Object Detection and Localization; Image Segmentation; Graph Convolutions
 Control code
 on1027476936
 Dimensions
 unknown
 Edition
 First edition
 Extent
 1 online resource
 File format
 unknown
 Form of item
 online
 Isbn
 9781491980422
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Quality assurance targets
 unknown
 Sound
 unknown sound
 Specific material designation
 remote
 System control number
 (OCoLC)1027476936
Library Links
Embed (Experimental)
Settings
Select options that apply then copy and paste the RDF/HTML data fragment to include in your application
Embed this data in a secure (HTTPS) page:
Layout options:
Include data citation:
<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.massey.ac.nz/portal/TensorFlowfordeeplearningfromlinear/aF6PopWR868/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.massey.ac.nz/portal/TensorFlowfordeeplearningfromlinear/aF6PopWR868/">TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.massey.ac.nz/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.massey.ac.nz/">Massey University Library, University of New Zealand</a></span></span></span></span></div>
Note: Adjust the width and height settings defined in the RDF/HTML code fragment to best match your requirements
Preview
Cite Data  Experimental
Data Citation of the Item TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh
Copy and paste the following RDF/HTML data fragment to cite this resource
<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.massey.ac.nz/portal/TensorFlowfordeeplearningfromlinear/aF6PopWR868/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.massey.ac.nz/portal/TensorFlowfordeeplearningfromlinear/aF6PopWR868/">TensorFlow for deep learning : from linear regression to reinforcement learning, Bharath Ramsundar and Reza Bosagh Zadeh</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.massey.ac.nz/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.massey.ac.nz/">Massey University Library, University of New Zealand</a></span></span></span></span></div>