内容简介

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder 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.

作品目录

Acknowledgments xv

Notation xix

1 Introduction 1

1.1 Who Should Read This Book? 8

1.2 Historical Trend sin Deep Learning 12

I Applied Math and Machine Learning Basics 27

2 Linear Algebra 29

2.1 Scalars, Vectors, Matrices and Tensors 29

2.2 Multiplying Matricesand Vectors 32

2.3 Identity and Inverse Matrices 34

2.4 Linear Dependence and Span 35

2.5 Norms 36

2.6 Special Kinds of Matrices and Vectors 38

2.7 Eigendecomposition 39

2.8 Singular Value Decomposition 42

2.9 The Moore-Penrose Pseudoinverse 43

2.10 The Trace Operator 44

2.11 The Determinant 45

2.12 Example: Principal Components Analysis 45

3 Probability and Information Theory 51

3.1 Why Probability? 52

3.2 Random Variables 54

3.3 Probability Distributions 54

3.4 Marginal Probability 56

3.5 ConditionalProbability 57

3.6 The Chain Rule of Conditional Probabilities 57

3.7 Independence and Conditional Independence 58

3.8 Expectation, Varianceand Covariance 58

3.9 Common Probability Distributions 60

3.10 UsefulPropertiesofCommonFunctions 65

3.11 Bayes’Rule 68

3.12 Technical Details of Continuous Variables 68

3.13 Information Theory 70

3.14 Structured Probabilistic Models 74

4 Numerical Computation 77

4.1 Overflow and Underflow 77

4.2 Poor Conditioning 79

4.3 Gradient-Based Optimization 79

4.4 Constrained Optimization 89

4.5 Example: Linear Least Squares 92

5 Machine Learning Basics 95

5.1 Learning Algorithms 96

5.2 Capacity, Overfitting and Underfitting 107

5.3 Hyperparameters and Validation Sets 117

5.4 Estimators, Bias and Variance 119

5.5 Maximum Likelihood Estimation 128

5.6 BayesianStatistics132

5.7 Supervised Learning Algorithms 136

5.8 Unsupervised Learning Algorithms142

5.9 StochasticGradientDescent 147

5.10 Building a Machine Learning Algorithm 149

5.11 Challenges Motivating Deep Learning 151

II Deep Networks: Modern Practices 161

6 Deep Feedforward Networks 163

6.1 Example:Learning XOR 166

6.2 Gradient-Based Learning 171

6.3 Hidden Units 185

6.4 Architecture Design 191

6.5 Back-Propagation and Other Dierentiation Algorithms 197

6.6 Historical Notes 217

7 Regularization for Deep Learning 221

7.1 Parameter Norm Penalties 223

7.2 Norm Penalties as Constrained Optimization 230

7.3 Regularization and Under-Constrained Problems 232

7.4 Dataset Augmentation 233

7.5 Noise Robustness 235

7.6 Semi-Supervised Learning236

7.7 Multitask Learning 237

7.8 Early Stopping 239

7.9 Parameter Tying and Parameter Sharing 246

7.10 Sparse Representations 247

7.11 Bagging and Other Ensemble Methods 249

7.12 Dropout 251

7.13 Adversarial Training261

7.14 Tangent Distance, Tangent Prop and Manifold Tangent Classiffer 263

8 Optimization for Training DeepModels 267

8.1 How Learning Differs from Pure Optimization 268

8.2 Challenges in Neural Network Optimization 275

8.3 Basic Algorithms 286

8.4 Parameter Initialization Strategies 292

8.5 Algorithms with Adaptive Learning Rates 298

8.6 Approximate Second-Order Methods 302

8.7 Optimization Strategies and Meta-Algorithms 309

9 Convolutional Networks 321

9.1 The Convolution Operation 322

9.2 Motivation 324

9.3 Pooling 330

9.4 Convolution and Pooling as an Infinitely Strong Prior 334

9.5 Variants of the Basic Convolution Function 337

9.6 Structured Outputs 347

9.7 Data Types 348

9.8 Efficient Convolution Algorithms 350

9.9 Random or Unsupervised Features 351

9.10 The Neuroscientific Basis for Convolutional Networks 353

9.11 Convolutional Networks and the History of Deep Learning 359

10 Sequence Modeling: Recurrent and Recursive Nets 363

10.1 Unfolding Computational Graphs 365

10.2 Recurrent Neural Networks 368

10.3 Bidirectional RNNs 383

10.4 Encoder-Decoder Sequence-to-Sequence Architectures 385

10.5 Deep Recurrent Networks 387

10.6 Recursive Neural Networks 388

10.7 The Challenge of Long-Term Dependencies 390

10.8 Echo State Networks 392

10.9 Leaky Units and Other Strategies for Multiple Time Scales 395

10.10 The Long Short-Term Memory and Other Gated RNNs 397

10.11 Optimization for Long-Term Dependencies 401

10.12 Explicit Memory 405

11 Practical Methodology 409

11.1 Performance Metrics 410

11.2 DefaultBaselineModels 413

11.3 Determining Whether to Gather More Data 414

11.4 Selecting Hyperparameters 415

11.5 Debugging Strategies 424

11.6 Example: Multi-Digit Number Recognition 428

12 Applications 431

12.1 Large-Scale Deep Learning 431

12.2 Computer Vision.440

12.3 Speech Recognition 446

12.4 Natural Language Processing 448

12.5 Other Applications 465

III Deep Learning Research 475

13 Linear Factor Models 479

13.1 Probabilistic PCA and Factor Analysis 480

13.2 Independent Component Analysis (ICA) 481

13.3 Slow Feature Analysis.484

13.4 Sparse Coding 486

13.5 Manifold Interpretation of PCA 489

14 Autoencoders 493

14.1 Undercomplete Autoencoders 494

14.2 Regularized Autoencoders 495

14.3 Representational Power, Layer Size and Depth 499

14.4 Stochastic Encodersand Decoders 500

14.5 Denoising Autoencoders501

14.6 Learning Manifolds with Autoencoders 506

14.7 Contractive Autoencoders 510

14.8 Predictive Sparse Decomposition 514

14.9 Applications of Autoencoders515

15 Representation Learning 517

15.1 Greedy Layer-Wise Unsupervised Pretraining 519

15.2 Transfer Learning and Domain Adaptation 526

15.3 Semi-Supervised Disentangling of Causal Factors 532

15.4 Distributed Representation 536

15.5 Exponential Gains from Depth 543

15.6 Providing Clues to Discover Underlying Causes 544

16 Structured Probabilistic Models for Deep Learning 549

16.1 The Challenge of Unstructured Modeling 550

16.2 Using Graphs to Describe Model Structure 554

16.3 Sampling from Graphical Models 570

16.4 Advantages of Structured Modeling 572

16.5 Learning about Dependencies 572

16.6 Inferenceand Approximate Inference 573

16.7 The Deep Learning Approach to Structured Probabilistic Models 575

17 Monte Carlo Methods 581

17.1 Sampling and Monte Carlo Methods 581

17.2 Importance Sampling 583

17.3 Markov Chain Monte Carlo Methods 586

17.4 Gibbs Sampling 590

17.5 The Challenge of Mixing between Separated Modes 591

18 Confronting the Partition Function 597

18.1 The Log-Likelihood Gradient 598

18.2 Stochastic Maximum Likelihood and Contrastive Divergence 599

18.3 Pseudolikelihood 607

18.4 Score Matching and Ratio Matching 609

18.5 DenoisingScore Matching 611

18.6 Noise-Contrastive Estimation 612

18.7 Estimatingthe Partition Function 614

19 Approximate Inference 623

19.1 Inferenceas Optimization 624

19.2 Expectation Maximization 626

19.3 MAP Inferenceand Sparse Coding 627

19.4 Variational Inferenceand Learning 629

19.5 Learned Approximate Inference 642

20 Deep Generative Models 645

20.1 Boltzmann Machines 645

20.2 Restricted Boltzmann Machines 647

20.3 Deep Belief Networks 651

20.4 Deep Boltzmann Machines 654

20.5 Boltzmann Machines for Real-Valued Data 667

20.6 Convolutional Boltzmann Machines 673

20.7 Boltzmann Machines for Structured or Sequential Outputs 675

20.8 Other Boltzmann Machines.677

20.9 Back-Propagation through Random Operations 678

20.10 Directed Generative Nets 682

20.11 Drawing Samples from Autoencoders 701

20.12 Generative Stochastic Networks 704

20.13 Other Generation Schemes 706

20.14 Evaluating Generative Models 707

20.15 Conclusion 710

Bibliography 711

Index 767