Deep Learning 1st Edition by Ian Goodfellow, Yoshua Bengio, Aaron Courville 0262035618 9780262035613

Deep Learning 1st Edition by Ian Goodfellow, Yoshua Bengio, Aaron Courville – Ebook PDF Instant Download/Delivery. 0262035618, 9780262035613

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ISBN 10: 0262035618

ISBN 13: 9780262035613

Author: Ian Goodfellow; Yoshua Bengio; Aaron Courville

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning 1st Table of contents:

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

Multitask 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: Recurrentand 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 MultipleTime 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

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