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Book Cover
Author Ramsundar, Bharath, author

Title TensorFlow for deep learning : from linear regression to reinforcement learning / Bharath Ramsundar and Reza Bosagh Zadeh
Edition First edition
Published Beijing : O'Reilly Media, 2018
Online access available from:
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Description 1 online resource
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
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
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
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
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
Notes Includes index
Online resource; title from PDF title page (EBSCO, viewed March 7, 2018)
Subject TensorFlow (Electronic resource)
Artificial intelligence.
Machine learning.
Reinforcement learning.
Genre/Form COMPUTERS / Desktop Applications / General.
COMPUTERS / Machine Theory.
Form Electronic book
Author Bosagh Zadeh, Reza, author
ISBN 1491980400 (electronic bk.)
9781491980408 (electronic bk.)
9781491980422 (electronic bk.)
1491980427 (electronic bk.)