Fundamentals of Deep Learning. 2nd Edition

Spis treści ebooka

• Preface
  • Prerequisites and Objectives
  • How Is This Book Organized?
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgements
    • Nithin and Nikhil
    • Joe
• 1. Fundamentals of Linear Algebra for Deep Learning
  • Data Structures and Operations
    • Matrix Operations
    • Vector Operations
    • Matrix-Vector Multiplication
  • The Fundamental Spaces
    • The Column Space
    • The Null Space
  • Eigenvectors and Eigenvalues
  • Summary
• 2. Fundamentals of Probability
  • Events and Probability
  • Conditional Probability
  • Random Variables
  • Expectation
  • Variance
  • Bayes Theorem
  • Entropy, Cross Entropy, and KL Divergence
  • Continuous Probability Distributions
  • Summary
• 3. 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
  • Summary
• 4. 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
• 5. Implementing Neural Networks in PyTorch
  • Introduction to PyTorch
  • Installing PyTorch
  • PyTorch Tensors
    • Tensor Init
    • Tensor Attributes
    • Tensor Operations
  • Gradients in PyTorch
  • The PyTorch nn Module
  • PyTorch Datasets and Dataloaders
  • Building the MNIST Classifier in PyTorch
  • Summary
• 6. 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
    • AdaGradAccumulating Historical Gradients
    • RMSPropExponentially Weighted Moving Average of Gradients
    • AdamCombining Momentum and RMSProp
  • The Philosophy Behind Optimizer Selection
  • Summary
• 7. Convolutional Neural Networks
  • Neurons in Human Vision
  • The Shortcomings of Feature Selection
  • Vanilla Deep Neural Networks Dont 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
  • Group Normalization for Memory Constrained Learning Tasks
  • Building a Convolutional Network for CIFAR-10
  • Visualizing Learning in Convolutional Networks
  • Residual Learning and Skip Connections for Very Deep Networks
  • Building a Residual Network with Superhuman Vision
  • Leveraging Convolutional Filters to Replicate Artistic Styles
  • Learning Convolutional Filters for Other Problem Domains
  • Summary
• 8. Embedding and Representation Learning
  • Learning Lower-Dimensional Representations
  • Principal Component Analysis
  • Motivating the Autoencoder Architecture
  • Implementing an Autoencoder in PyTorch
  • 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
• 9. 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 Units
  • PyTorch 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
  • Self-Attention and Transformers
  • Summary
• 10. Generative Models
  • Generative Adversarial Networks
  • Variational Autoencoders
  • Implementing a VAE
  • Score-Based Generative Models
  • Denoising Autoencoders and Score Matching
  • Summary
• 11. Methods in Interpretability
  • Overview
  • Decision Trees and Tree-Based Algorithms
  • Linear Regression
  • Methods for Evaluating Feature Importance
    • Permutation Feature Importance
    • Partial Dependence Plots
  • Extractive Rationalization
  • LIME
  • SHAP
  • Summary
• 12. 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 PyTorch
  • Teaching a DNC to Read and Comprehend
  • Summary
• 13. Deep Reinforcement Learning
  • Deep Reinforcement Learning Masters Atari Games
  • What Is Reinforcement Learning?
  • Markov Decision Processes
    • Policy
    • Future Return
    • Discounted Future Return
  • Explore Versus Exploit
    • -Greedy
    • Annealed -Greedy
  • Policy Versus Value Learning
  • 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
  • Trust-Region Policy Optimization
  • Proximal Policy Optimization
  • Q-Learning and Deep Q-Networks
    • The Bellman Equation
    • Issues with Value Iteration
    • Approximating the Q-Function
    • Deep Q-Network
    • Training DQN
    • Learning Stability
    • Target Q-Network
    • Experience Replay
    • From Q-Function to Policy
    • DQN and the Markov Assumption
    • DQNs Solution to the Markov Assumption
    • Playing Breakout with 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
    • Asynchronous Advantage Actor-Critic Agent
    • UNsupervised REinforcement and Auxiliary Learning
  • Summary
• Index

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