Deep learning:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2016]
|
| Schriftenreihe: | Adaptive computation and machine learning
|
| Schlagworte: | |
| Online-Zugang: | Volltext Inhaltsverzeichnis Klappentext |
| Beschreibung: | Hier auch später erschienene, unveränderte Nachdrucke |
| Beschreibung: | xxii, 775 Seiten Illustrationen, Diagramme |
| ISBN: | 9780262035613 |
Internformat
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| 245 | 1 | 0 | |a Deep learning |c Ian Goodfellow, Yoshua Bengio and Aaron Courville |
| 264 | 1 | |a Cambridge, Massachusetts ; London, England |b The MIT Press |c [2016] | |
| 264 | 4 | |c © 2016 | |
| 300 | |a xxii, 775 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 490 | 0 | |a Adaptive computation and machine learning | |
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
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| 700 | 1 | |a Courville, Aaron |e Verfasser |0 (DE-588)1171761546 |4 aut | |
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Datensatz im Suchindex
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Contents
Website xiii
Acknowledgments xv
Notation xix
1 Introduction 1
1.1 Who Should Read This Book?. 8
1.2 Historical Trends in Deep Learning. 12
1 Applied Math and Machine Learning Basics 27
2 Linear Algebra 29
2.1 Scalars, Vectors, Matrices and Tensors. 29
2.2 Multiplying Matrices and 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 Conditional Probability. 57
3.6 The Chain Rule of Conditional Probabilities. 57
s
3.7 Independence and Conditional Independence. 58
3.8 Expectation, Variance and Covariance. 58
3.9 Common Probability Distributions. 60
3.10 Useful Properties of Common Functions. 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 Bayesian Statistics.132
5.7 Supervised Learning Algorithms.136
5.8 Unsupervised Learning Algorithms.142
5.9 Stochastic Gradient Descent .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 Differentiation
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 Learning.236
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 Training.261
7.14 Tangent Distance, Tangent Prop and Manifold Tangent Classifier . 263
8 Optimization for Training Deep Models 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 Default Baseline Models.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 PC A 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 Encoders and Decoders.500
14.5 Denoising Autoencoders.501
14.6 Learning Manifolds with Autoencoders .506
14.7 Contractive Autoencoders.510
14.8 Predictive Sparse Decomposition.514
14.9 Applications of Autoencoders.515
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 Inference and 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 Denoising Score Matching.611
18.6 Noise-Contrastive Estimation.612
18.7 Estimating the Partition Function.614
19 Approximate Inference 623
19.1 Inference as Optimization.624
19.2 Expectation Maximization .626
19.3 MAP Inference and Sparse Coding.627
19.4 Variational Inference and 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
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 knowl-
edge 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 rec-
ognition, computer vision, online recommendation systems, bioinformatics, and video games. Final-
ly, 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.
Ian Goodfellow is Research Scientist at OpenAI. Yoshua Bengio is Professor of Computer Science
at the Université de Montréal. Aaron Courville is Assistant Professor of Computer Science at the
Université de Montréal.
Adaptive Computation and Machine Learning series |
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| id | DE-604.BV043819519 |
| illustrated | Illustrated |
| indexdate | 2024-12-28T04:09:26Z |
| institution | BVB |
| isbn | 9780262035613 |
| language | English |
| lccn | 2016022992 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029230557 |
| oclc_num | 969648672 |
| open_access_boolean | 1 |
| owner | DE-11 DE-473 DE-BY-UBG DE-29T DE-860 DE-19 DE-BY-UBM DE-739 DE-384 DE-355 DE-BY-UBR DE-29 DE-83 DE-91G DE-BY-TUM DE-526 DE-863 DE-BY-FWS DE-20 DE-188 DE-1049 DE-B768 DE-706 DE-1050 DE-898 DE-BY-UBR DE-525 DE-M347 DE-945 DE-92 DE-1028 DE-703 DE-Aug4 DE-861 DE-523 DE-634 DE-859 DE-1046 DE-91 DE-BY-TUM DE-522 DE-862 DE-BY-FWS DE-858 DE-N2 DE-573 |
| owner_facet | DE-11 DE-473 DE-BY-UBG DE-29T DE-860 DE-19 DE-BY-UBM DE-739 DE-384 DE-355 DE-BY-UBR DE-29 DE-83 DE-91G DE-BY-TUM DE-526 DE-863 DE-BY-FWS DE-20 DE-188 DE-1049 DE-B768 DE-706 DE-1050 DE-898 DE-BY-UBR DE-525 DE-M347 DE-945 DE-92 DE-1028 DE-703 DE-Aug4 DE-861 DE-523 DE-634 DE-859 DE-1046 DE-91 DE-BY-TUM DE-522 DE-862 DE-BY-FWS DE-858 DE-N2 DE-573 |
| physical | xxii, 775 Seiten Illustrationen, Diagramme |
| psigel | ebook |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | The MIT Press |
| record_format | marc |
| series2 | Adaptive computation and machine learning |
| spellingShingle | Goodfellow, Ian 1987- Bengio, Yoshua Courville, Aaron Deep learning Machine learning Künstliche Intelligenz (DE-588)4033447-8 gnd Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)1135597375 (DE-588)4193754-5 |
| title | Deep learning |
| title_auth | Deep learning |
| title_exact_search | Deep learning |
| title_full | Deep learning Ian Goodfellow, Yoshua Bengio and Aaron Courville |
| title_fullStr | Deep learning Ian Goodfellow, Yoshua Bengio and Aaron Courville |
| title_full_unstemmed | Deep learning Ian Goodfellow, Yoshua Bengio and Aaron Courville |
| title_short | Deep learning |
| title_sort | deep learning |
| topic | Machine learning Künstliche Intelligenz (DE-588)4033447-8 gnd Deep learning (DE-588)1135597375 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Machine learning Künstliche Intelligenz Deep learning Maschinelles Lernen |
| url | http://www.deeplearningbook.org/ http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029230557&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029230557&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT goodfellowian deeplearning AT bengioyoshua deeplearning AT courvilleaaron deeplearning |
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