Deep Learning
Ian Goodfellow
Yoshua Bengio
Aaron Courville
Contents
Website vii
Acknowledgments viii
Notation xi
1 Introduction 1
1.1 Who Should Read This Book? ...... ......... ..... 8
1.2 Historical Trends in Deep Learning ......... ........ 11
I Applied Math and Machine Learning Basics 29
2 Linear Algebra 31
2.1 Scalars, Vectors, Matrices and Tensors ......... ...... 31
2.2 Multiplying Matrices and Vectors ......... ........ . 34
2.3 Identity and Inverse Matrices ......... ........ ... 36
2.4 Linear Dependence and Span ......... ........ ... 37
2.5 Norms ......... ........ ........ ........ 39
2.6 Special Kinds of Matrices and Vectors ............... 40
2.7 Eigendecomposition .......... ........ ........ 42
2.8 Singular Value Decomposition ........ ........ .... 44
2.9 The Moore-Penrose Pseudoinverse ......... ........ . 45
2.10 The Trace Operator ......... ........ ........ 46
2.11 The Determinant .. ........ ........ ......... 47
2.12 Example: Principal Components Analysis ......... .... 48
3 Probability and Information Theory 53
3.1 Why Probability? ..... ......... ........ ..... 54
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CONTENTS
3.2 Random Variables ..... ........ ......... .... 56
3.3 Probability Distributions ......... ........ ...... 56
3.4 Marginal Probability ......... ......... ....... 58
3.5 Conditional Probability .. ........ ........ ..... 59
3.6 The Chain Rule of Conditional Probabilities ......... ... 59
3.7 Independence and Conditional Independence ......... ... 60
3.8 Expectation, Variance and Covariance .......... ..... 60
3.9 Common Probability Distributions ............... .. 62
3.10 Useful Properties of Common Functions ... ......... .. 67
3.11 Bayes’ Rule .......... ........ ........ .... 70
3.12 Technical Details of Continuous Variables ...... ....... 71
3.13 Information Theory .......... ........ ........ 72
3.14 Structured Probabilistic Models .... ........ ....... 75
4 Numerical Computation 80
4.1 Overflow and Underflow ......... ........ ...... 80
4.2 Poor Conditioning ......... ........ ......... 82
4.3 Gradient-Based Optimization ....... ........ ..... 82
4.4 Constrained Optimization ............. ........ . 93
4.5 Example: Linear Least Squares ....... ......... ... 96
5 Machine Learning Basics 98
5.1 Learning Algorithms ........... ........ ...... 99
5.2 Capacity, Overfitting and Underfitting .. ........ ..... 110
5.3 Hyperparameters and Validation Sets . ........ ....... 120
5.4 Estimators, Bias and Variance ...... ........ ...... 122
5.5 Maximum Likelihood Estimation ...... ......... ... 131
5.6 Bayesian Statistics ........... ........ ....... 135
5.7 Supervised Learning Algorithms ... ........ ........ 139
5.8 Unsupervised Learning Algorithms ............... .. 145
5.9 Stochastic Gradient Descent .... ......... ........ 150
5.10 Building a Machine Learning Algorithm ............. . 152
5.11 Challenges Motivating Deep Learning ..... ......... .. 154
II Deep Networks: Modern Practices 165
6 Deep Feedforward Networks 167
6.1 Example: Learning XOR . ......... ........ ..... 170
6.2 Gradient-Based Learning . ........ ........ ...... 176
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6.3 Hidden Units ...... ........ ......... ...... 190
6.4 Architecture Design ......... ........ ........ . 196
6.5 Back-Propagation and Other Differentiation Algorithms ..... 203
6.6 Historical Notes ....... ........ ......... .... 224
7 Regularization for Deep Learning 228
7.1 Parameter Norm Penalties ..... ......... ........ 230
7.2 Norm Penalties as Constrained Optimization ........ .... 237
7.3 Regularization and Under-Constrained Problems .. ....... 239
7.4 Dataset Augmentation .......... ......... ..... 240
7.5 Noise Robustness ......... ........ ........ .. 242
7.6 Semi-Supervised Learning ................ ...... 244
7.7 Multi-Task Learning .............. ......... .. 245
7.8 Early Stopping ......... ........ ........ ... 246
7.9 Parameter Tying and Parameter Sharing.............. 251
7.10 Sparse Representations ......... ........ ....... 253
7.11 Bagging and Other Ensemble Methods . ......... ..... 255
7.12 Dropout ........ ......... ........ ....... 257
7.13 Adversarial Training ........ ......... ........ 267
7.14 Tangent Distance, Tangent Prop, and Manifold Tangent Classifier 268
8 Optimization for Training Deep Models 274
8.1 How Learning Differs from Pure Optimization ........... 275
8.2 Challenges in Neural Network Optimization ..... ....... 282
8.3 Basic Algorithms ............. ........ ...... 294
8.4 Parameter Initialization Strategies . ......... ....... 301
8.5 Algorithms with Adaptive Learning Rates ....... ...... 306
8.6 Approximate Second-Order Methods .... ......... ... 310
8.7 Optimization Strategies and Meta-Algorithms ..... ...... 318
9 Convolutional Networks 331
9.1 The Convolution Operation ................ ..... 332
9.2 Motivation .. ........ ......... ........ .... 336
9.3 Pooling ............. ........ ......... ... 340
9.4 Convolution and Pooling as an Infinitely Strong Prior .. ..... 346
9.5 Variants of the Basic Convolution Function ............ 348
9.6 Structured Outputs . ........ ......... ........ 359
9.7 Data Types ...... ........ ........ ........ 361
9.8 Efficient Convolution Algorithms ........ ........ .. 363
9.9 Random or Unsupervised Features ........ ........ . 364
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9.10 The Neuroscientific Basis for Convolutional Networks ...... . 365
9.11 Convolutional Networks and the History of Deep Learning .... 372
10Sequence Modeling: Recurrent and Recursive Nets 374
10.1 Unfolding Computational Graphs ............. ..... 376
10.2 Recurrent Neural Networks ... ......... ........ . 379
10.3 Bidirectional RNNs.............. ......... ... 396
10.4 Encoder-Decoder Sequence-to-Sequence Architectures ...... . 397
10.5 Deep Recurrent Networks ........ ......... ..... 399
10.6 Recursive Neural Networks ..... ......... ........ 401
10.7 The Challenge of Long-Term Dependencies .......... ... 403
10.8 Echo State Networks .......... ......... ...... 406
10.9 Leaky Units and Other Strategies for Multiple Time Scales ... . 409
10.10The Long Short-Term Memory and Other Gated RNNs .. .... 411
10.11Optimization for Long-Term Dependencies ........ ..... 415
10.12Explicit Memory .......... ......... ........ 419
11Practical methodology 424
11.1 Performance Metrics .......... ........ ....... 425
11.2 Default Baseline Models ........ ........ ....... 428
11.3 Determining Whether to Gather More Data ............ 429
11.4 Selecting Hyperparameters ......... ........ ..... 430
11.5 Debugging Strategies ..... ........ ......... ... 439
11.6 Example: Multi-Digit Number Recognition ..... ........ 443
12Applications 446
12.1 Large Scale Deep Learning . ........ ......... .... 446
12.2 Computer Vision ......... ........ ........ .. 455
12.3 Speech Recognition...... ........ ......... ... 461
12.4 Natural Language Processing ... ........ ........ . 464
12.5 Other Applications ......... ........ ........ . 480
III Deep Learning Research 489
13Linear Factor Models 492
13.1 Probabilistic PCA and Factor Analysis ....... ........ 493
13.2 Independent Component Analysis (ICA) ............ .. 494
13.3 Slow Feature Analysis ...... ......... ........ . 496
13.4 Sparse Coding ...... ........ ......... ...... 499
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