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Goodfellow Ian, Bengio Yoshua, Courville Aaron. Deep Learning Book

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Goodfellow Ian, Bengio Yoshua, Courville Aaron. Deep Learning Book
MIT Press, 2016. — 802 p.
A comprehensive introduction to neural networks and deep learning by leading researchers of this field. Written for two main target audiences: university students (undergraduate or graduate) learning about machine learning, and software engineers.
This is a PDF compilation of online book (www.deeplearningbook.org)
Introduction
Who Should Read This Book?
Historical Trends in Deep Learning
Applied Math and Machine Learning Basics
Linear Algebra
Scalars, Vectors, Matrices and Tensors
Multiplying Matrices and Vectors
Identity and Inverse Matrices
Linear Dependence and Span
Norms
Special Kinds of Matrices and Vectors
Eigendecomposition
Singular Value Decomposition
The Moore-Penrose Pseudoinverse
The Trace Operator
The Determinant
Example: Principal Components Analysis
Probability and Information Theory
Why Probability?
Random Variables
Probability Distributions
Marginal Probability
Conditional Probability
The Chain Rule of Conditional Probabilities
Independence and Conditional Independence
Expectation, Variance and Covariance
Common Probability Distributions
Useful Properties of Common Functions
Bayes’ Rule
Technical Details of Continuous Variables
Information Theory
Structured Probabilistic Models
Numerical Computation
Overflow and Underflow
Poor Conditioning
Gradient-Based Optimization
Constrained Optimization
Example: Linear Least Squares
Machine Learning Basics
Learning Algorithms
Capacity, Overfitting and Underfitting
Hyperparameters and Validation Sets
Estimators, Bias and Variance
Maximum Likelihood Estimation
Bayesian Statistics
Supervised Learning Algorithms
Unsupervised Learning Algorithms
Stochastic Gradient Descent
Building a Machine Learning Algorithm
Challenges Motivating Deep Learning
Deep Networks: Modern Practices
Deep Feedforward Networks
Example: Learning XOR
Gradient-Based Learning
Hidden Units
Architecture Design
Back-Propagation and Other Differentiation Algorithms
Historical Notes
Regularization for Deep Learning
Parameter Norm Penalties
Norm Penalties as Constrained Optimization
Regularization and Under-Constrained Problems
Dataset Augmentation
Noise Robustness
Semi-Supervised Learning
Multi-Task Learning
Early Stopping
Parameter Tying and Parameter Sharing
Sparse Representations
Bagging and Other Ensemble Methods
Dropout
Adversarial Training
Tangent Distance, Tangent Prop, and Manifold Tangent Classifier
Optimization for Training Deep Models
How Learning Differs from Pure Optimization
Challenges in Neural Network Optimization
Basic Algorithms
Parameter Initialization Strategies
Algorithms with Adaptive Learning Rates
Approximate Second-Order Methods
Optimization Strategies and Meta-Algorithms
Convolutional Networks
The Convolution Operation
Motivation
Pooling
Convolution and Pooling as an Infinitely Strong Prior
Variants of the Basic Convolution Function
Structured Outputs
Data Types
Efficient Convolution Algorithms
Random or Unsupervised Features
The Neuroscientific Basis for Convolutional Networks
Convolutional Networks and the History of Deep Learning
Sequence Modeling: Recurrent and Recursive Nets
Unfolding Computational Graphs
Recurrent Neural Networks
Bidirectional RNNs
Encoder-Decoder Sequence-to-Sequence Architectures
Deep Recurrent Networks
Recursive Neural Networks
The Challenge of Long-Term Dependencies
Echo State Networks
Leaky Units and Other Strategies for Multiple Time Scales
The Long Short-Term Memory and Other Gated RNNs
Optimization for Long-Term Dependencies
Explicit Memory
Practical Methodology
Performance Metrics
Default Baseline Models
Determining Whether to Gather More Data
Selecting Hyperparameters
Debugging Strategies
Example: Multi-Digit Number Recognition
Applications
Large Scale Deep Learning
Computer Vision
Speech Recognition
Natural Language Processing
Other Applications
Deep Learning Research
Linear Factor Models
Probabilistic PCA and Factor Analysis .
Independent Component Analysis (ICA)
Slow Feature Analysis
Sparse Coding
Manifold Interpretation of PCA
Autoencoders
Undercomplete Autoencoders
Regularized Autoencoders
Representational Power, Layer Size and Depth
Stochastic Encoders and Decoders
Denoising Autoencoders
Learning Manifolds with Autoencoders
Contractive Autoencoders
Predictive Sparse Decomposition
Applications of Autoencoders
Representation Learning
Greedy Layer-Wise Unsupervised Pretraining
Transfer Learning and Domain Adaptation
Semi-Supervised Disentangling of Causal Factors
Distributed Representation
Exponential Gains from Depth
Providing Clues to Discover Underlying Causes
Structured Probabilistic Models for Deep Learning
The Challenge of Unstructured Modeling
Using Graphs to Describe Model Structure
Sampling from Graphical Models
Advantages of Structured Modeling
Learning about Dependencies
Inference and Approximate Inference
The Deep Learning Approach to Structured Probabilistic Models
Monte Carlo Methods
Sampling and Monte Carlo Methods
Importance Sampling
Markov Chain Monte Carlo Methods
Gibbs Sampling
The Challenge of Mixing between Separated Modes
Confronting the Partition Function
The Log-Likelihood Gradient
Stochastic Maximum Likelihood and Contrastive Divergence
Pseudolikelihood
Score Matching and Ratio Matching
Denoising Score Matching
Noise-Contrastive Estimation
Estimating the Partition Function
Approximate Inference
Inference as Optimization
Expectation Maximization
MAP Inference and Sparse Coding
Variational Inference and Learning
Learned Approximate Inference
Deep Generative Models
Boltzmann Machines
Restricted Boltzmann Machines
Deep Belief Networks
Deep Boltzmann Machines
Boltzmann Machines for Real-Valued Data
Convolutional Boltzmann Machines
Boltzmann Machines for Structured or Sequential Outputs
Other Boltzmann Machines
Back-Propagation through Random Operations
Directed Generative Nets
Drawing Samples from Autoencoders
Generative Stochastic Networks
Other Generation Schemes
Evaluating Generative Models
Conclusion
Bibliography
Index
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