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深度学习分析.docx
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Deep Learning
Ian Goodfellow
Yoshua Bengio
Aaron Courville
![](https://csdnimg.cn/release/download_crawler_static/89214838/bg2.jpg)
i
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
![](https://csdnimg.cn/release/download_crawler_static/89214838/bg3.jpg)
ii
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
![](https://csdnimg.cn/release/download_crawler_static/89214838/bg4.jpg)
ii
i
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
.........................................................................243
7.7
Multi-Task Learning..................................................................................
244
7.8
Early Stopping ...........................................................................................246
7.9
Parameter Tying and Parameter Sharing..............................................253
7.10
Sparse
Representations.............................................................................254
7.11
Bagging and Other Ensemble Methods..................................................256
7.12
Dropout.......................................................................................................258
7.13
Adversarial Training .................................................................................268
7.14
Tangent Distance, Tangent Prop, and Manifold Tangent Classifier 270
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 ......................................317
9
Convolutional
Networks
330
9.1
The Convolution Operation ......................................................................331
9.2
Motivation...................................................................................................335
9.3
Pooling ........................................................................................................339
9.4
Convolution and Pooling as an Infinitely Strong Prior........................345
9.5
Variants of the Basic Convolution Function..........................................347
9.6
Structured Outputs ...................................................................................358
9.7
Data Types .................................................................................................360
9.8
Efficient Convolution Algorithms............................................................362
9.9
Random
or
Unsupervised
Features
..........................................................363
![](https://csdnimg.cn/release/download_crawler_static/89214838/bg5.jpg)
i
v
9.10
The Neuroscientific Basis for Convolutional Networks........................364
9.11
Convolutional Networks and the History of Deep Learning ................371
10
Sequence
Modeling:
Recurrent
and
Recursive
Nets
373
10.1
Unfolding
Computational
Graphs
............................................................375
10.2
Recurrent Neural Networks .....................................................................378
10.3
Bidirectional
RNNs....................................................................................395
10.4
Encoder-Decoder
Sequence-to-Sequence
Architectures
.........................396
10.5
Deep Recurrent Networks........................................................................398
10.6
Recursive Neural
Networks
......................................................................400
10.7
The Challenge of Long-Term Dependencies .........................................402
10.8
Echo State
Networks
.................................................................................405
10.9
Leaky Units and Other Strategies for Multiple Time Scales..............408
10.10
The Long Short-Term Memory and Other Gated RNNs.....................410
10.11
Optimization for Long-Term Dependencies...........................................414
10.12
Explicit Memory.........................................................................................
418
11
Practical Methodology
423
11.1
Performance
Metrics..................................................................................
424
11.2
Default Baseline Models ...........................................................................427
11.3
Determining Whether to Gather More Data.........................................428
11.4
Selecting
Hyperparameters
........................................................................429
11.5
Debugging
Strategies
.................................................................................438
11.6
Example: Multi-Digit Number
Recognition
...........................................442
12
Applications 445
12.1
Large Scale Deep Learning.......................................................................445
12.2
Computer Vision........................................................................................454
12.3
Speech
Recognition...................................................................................460
12.4
Natural Language
Processing
...................................................................463
12.5
Other Applications.....................................................................................479
III
Deep
Learning
Research
488
13
Linear
Factor
Models
491
13.1
Probabilistic PCA and Factor Analysis .................................................492
13.2
Independent
Component
Analysis
(ICA)
................................................493
13.3
Slow Feature Analysis................................................................................
495
13.4
Sparse
Coding ............................................................................................498
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