TensorFlow for Deep Learning

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

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

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

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

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

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

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