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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

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
Creator
Contributor
Author
Subject
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
Instantiates
Publication
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 Short-Term Memory Cells; Deep Learning Architectures; LeNet; AlexNet; ResNet; Neural Captioning Model; Google Neural Machine Translation; One-Shot 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
Publication
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 Short-Term Memory Cells; Deep Learning Architectures; LeNet; AlexNet; ResNet; Neural Captioning Model; Google Neural Machine Translation; One-Shot 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

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