深度学习基础

Preface
1. The Neural Network
Building Intelligent Machines
The Limits of Traditional Computer Programs
The Mechanics of Machine Learning
The Neuron
Expressing Linear Perceptrons as Neurons
Feed-Forward Neural Networks
Linear Neurons and Their Limitations
Sigmoid， Tanh， and ReLU Neurons
Softmax Output Layers
Looking Forward

2. Training Feed-Forward Neural Networks
The Fast-Food Problem
Gradient Descent
The Delta Rule and Learning Rates
Gradient Descent with Sigmoidal Neurons
The Backpropagation Algorithm
Stochastic and Minibatch Gradient Descent
Test Sets， Validation Sets， and Overfitting
Preventing Overfitting in Deep Neural Networks
Summary

3. Implementing Neural Networks in TensorFIow
What Is TensorFlow?
How Does TensorFlow Compare to Alternatives?
Installing TensorFlow
Creating and Manipulating TensorFlow Variables
TensorFlow Operations
Placeholder Tensors
Sessions in TensorFlow
Navigating Variable Scopes and Sharing Variables
Managing Models over the CPU and GPU
Specifying the Logistic Regression Model in TensorFlow
Logging and Training the Logistic Regression Model
Leveraging TensorBoard to Visualize Computation Graphs and Learning
Building a Multilayer Model for MNIST in TensorFlow
Summary

4. Beyond Gradient Descent
The Challenges with Gradient Descent
Local Minima in the Error Surfaces of Deep Networks
Model Identifiability
How Pesky Are Spurious Local Minima in Deep Networks?
Flat Regions in the Error Surface
When the Gradient Points in the Wrong Direction
Momentum-Based Optimization
A Brief View of Second-Order Methods
Learning Rate Adaptation
AdaGrad——Accumulating Historical Gradients
RMSProp——Exponentially Weighted Moving Average of Gradients
Adam——Combining Momentum and RMSProp
The Philosophy Behind Optimizer Selection
Summary

5. Convolutional Neural Networks
Neurons in Human Vision
The Shortcomings of Feature Selection
Vanilla Deep Neural Networks Don't Scale
Filters and Feature Maps
Full Description of the Convolutional Layer
Max Pooling
Full Architectural Description of Convolution Networks
Closing the Loop on MNIST with Convolutional Networks
Image Preprocessing Pipelines Enable More Robust Models
Accelerating Training with Batch Normalization
Building a Convolutional Network for CIFAR-10
Visualizing Learning in Convolutional Networks
Leveraging Convolutional Filters to Replicate Artistic Styles
Learning Convolutional Filters for Other Problem Domains
Summary

6. Embedding and Representation Learning
Learning Lower-Dimensional Representations
Principal Component Analysis
Motivating the Autoencoder Architecture
Implementing an Autoencoder in TensorFlow
Denoising to Force Robust Representations
Sparsity in Autoencoders
When Context Is More Informative than the Input Vector
The Word2Vec Framework
Implementing the Skip-Gram Architecture
Summary

7. Models for Sequence Analysis
Analyzing Variable-Length Inputs
Tackling seq2seq with Neural N-Grams
Implementing a Part-of-Speech Tagger
Dependency Parsing and SyntaxNet
Beam Search and Global Normalization
A Case for Stateful Deep Learning Models
Recurrent Neural Networks
The Challenges with Vanishing Gradients
Long Short-Term Memory （LSTM） Units
TensorFlow Primitives for RNN Models
Implementing a Sentiment Analysis Model
Solving seq2seq Tasks with Recurrent Neural Networks
Augmenting Recurrent Networks with Attention
Dissecting a Neural Translation Network
Summary

8. Memory Augmented Neural Networks
Neural Turing Machines
Attention-Based Memory Access
NTM Memory Addressing Mechanisms
Differentiable Neural Computers
Interference-Free Writing in DNCs
DNC Memory Reuse
Temporal Linking of DNC Writes
Understanding the DNC Read Head
The DNC Controller Network
Visualizing the DNC in Action
Implementing the DNC in TensorFlow
Teaching a DNC to Read and Comprehend
Summary

9. Deep Reinforcement Learning
Deep Reinforcement Learning Masters Atari Games
What Is Reinforcement Learning?
Markov Decision Processes （MDP）
Policy
Future Return
Discounted Future Return
Explore Versus Exploit
Policy Versus Value Learning
Policy Learning via Policy Gradients
Pole-Cart with Policy Gradients
OpenAI Gym
Creating an Agent
Building the Model and Optimizer
Sampling Actions
Keeping Track of History
Policy Gradient Main Function
PGAgent Performance on Pole-Cart
Q-Learning and Deep Q-Networks
The Bellman Equation
Issues with Value Iteration
Approximating the Q-Function
Deep Q-Network （DQN）
Training DQN
Learning Stability
Target Q-Network
Experience Replay
From Q-Function to Policy
DQN and the Markov Assumption
DQN's Solution to the Markov Assumption
Playing Breakout wth DQN
Building Our Architecture
Stacking Frames
Setting Up Training Operations
Updating Our Target Q-Network
Implementing Experience Replay
DQN Main Loop
DQNAgent Results on Breakout
Improving and Moving Beyond DQN
Deep Recurrent Q-Networks （DRQN）
Asynchronous Advantage Actor-Critic Agent （A3C）
UNsupervised REinforcement and Auxiliary Learning （UNREAL）
Summary
Index