Tensorflow for Deep Learning: From Linear Regression to Reinforcement Learning

Description

Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up. It's ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units Cover Copyright Table of Contents Preface Conventions Used in This Book Using Code Examples O’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 TensorFlow Review 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 TensorFlow Mathematical 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? “Neurons” 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 Metrics Multiclass 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 Generating Images with Variational Autoencoders Training a Convolutional Network in TensorFlow The MNIST Dataset Loading MNIST TensorFlow Convolutional Primitives The Convolutional Architecture Evaluating Trained Models Challenge for the Reader Review Chapter 7. Recurrent Neural Networks Overview of Recurrent Architectures Recurrent Cells Long Short-Term Memory (LSTM) Gated Recurrent Units (GRU) Applications of Recurrent Models Sampling from Recurrent Networks Seq2seq Models Neural Turing Machines Working with Recurrent Neural Networks in Practice Processing the Penn Treebank Corpus Code for Preprocessing Loading Data into TensorFlow The Basic Recurrent Architecture Challenge for the Reader Review Chapter 8. Reinforcement Learning Markov Decision Processes Reinforcement Learning Algorithms Q-Learning Policy Learning Asynchronous Training Limits of Reinforcement Learning Playing Tic-Tac-Toe Object Orientation Abstract Environment Tic-Tac-Toe Environment The Layer Abstraction Defining a Graph of Layers The A3C Algorithm The A3C Loss Function Defining Workers Training the Policy Challenge for the Reader Review Chapter 9. Training Large Deep Networks Custom Hardware for Deep Networks CPU Training GPU Training Tensor Processing Units Field Programmable Gate Arrays Neuromorphic Chips Distributed Deep Network Training Data Parallelism Model Parallelism Data Parallel Training with Multiple GPUs on Cifar10 Downloading and Loading the DATA Deep Dive on the Architecture Training on Multiple GPUs Challenge for the Reader Review Chapter 10. The Future of Deep Learning Deep Learning Outside the Tech Industry Deep Learning in the Pharmaceutical Industry Deep Learning in Law Deep Learning for Robotics Deep Learning in Agriculture Using Deep Learning Ethically Is Artificial General Intelligence Imminent? Where to Go from Here? Index About the Authors Colophon