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
Author(s): Bharath Ramsundar
Publisher: O'Reilly Media
Year: 2018
Language: English
Pages: 256
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
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