Understanding deep learning:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, Massachusetts ; London, England
The MIT Press
[2023]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Literaturverzeichnis Seite 462-511 |
| Beschreibung: | xi, 527 Seiten Illustrationen, Diagramme (teilweise farbig) |
| ISBN: | 9780262048644 |
Internformat
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Contents xiii Preface Acknowledgements xv 1 Introduction 1.1 Supervised learning. 1.2 Unsupervised learning . 1.3 Reinforcement learning. 1.4 Ethics . 1.5 Structure of book. 1.6 Other books. 1.7 How to read this book. 1 1 7 11 12 15 15 16 2 Supervised learning 17 2.1 Supervised learning overview. 17 2.2 Linear regression example. 18 2.3 Summary. 22 3 Shallow neural networks 25 3.1 Neural network example. 25 3.2 Universal approximation theorem. 29 3.3 Multivariate inputs and outputs. 30 3.4 Shallow neural networks: general
case. 33 3.5 Terminology. 35 3.6 Summary. 36 4 Deep neural networks 41 4.1 Composing neural networks. 41 4.2 From composing networks to deep networks. 43 4.3 Deep neural networks. 45 4.4 Matrix notation. 48 4.5 Shallow vs. deep neural networks. 49 4.6 Summary. 52
viii Contents functions 56 Maximum likelihood . 56 Recipe for constructing loss functions. 60 Example 1: univariate regression . 61 Example 2: binary classification. 64 Example 3: multiclass classification. 67 Multiple outputs . 69 Cross-entropy loss. 71 Summary. 72 5 Loss 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 6 Fitting models 77 6.1 Gradient descent . 77 6.2 Stochastic gradient descent. 83 6.3 Momentum . 86 6.4 Adam. 88 6.5 Training algorithm hyperparameters . 91 6.6 Summary. 91 7 Gradients
and initialization 96 7.1 Problem definitions. 96 7.2 Computing derivatives. 97 7.3 Toy example. 100 7.4 Backpropagation algorithm. 103 7.5 Parameter initialization. . 107 7.6 Example training code. Ill 7.7 Summary. Ill 8 Measuring performance 118 8.1 Training a simple model.118 8.2 Sources of error. 120 8.3 Reducing error. 124 8.4 Double descent . 127 8.5 Choosing hyperparameters.132 8.6 Summary. 133 9 Regularization 138 9.1 Explicit
regularization . 138 9.2 Implicit regularization . 141 9.3 Heuristics to improve performance.144 9.4 Summary. 154 10 Convolutional networks 161 10.1 Invariance and equivariance.161 10.2 Convolutional networks for IDinputs. 163 10.3 Convolutional networks for 2Dinputs. 170
Contents 10.4 10.5 10.6 ix Downsampling and upsampling. 171 Applications. 174 Summary. 179 11 Residual networks 186 11.1 Sequential processing. 186 11.2 Residual connections and residual blocks. 189 11.3 Exploding gradients in residual networks. 192 11.4 Batch normalization . 192 11.5 Common residual architectures . 195 11.6 Why do nets with residual connections perform so well? .199 11.7 Summary.199 12 Transformers 207 12.1 Processing text data. 207 12.2 Dot-product self-attention.208 12.3 Extensions to dot-product self-attention . 213 12.4
Transformers. 215 12.5 Transformers for natural language processing. 216 12.6 Encoder model example: BERT. 219 12.7 Decoder model example: GPT3. 222 12.8 Encoder-decoder model example: machine translation. 226 12.9 Transformers for long sequences. 227 12.10 Transformers for images .228 12.11 Summary. 232 13 Graph neural networks 240 13.1 What is a graph?.240 13.2 Graph representation.243 13.3 Graph neural networks, tasks, and loss functions.245 13.4 Graph convolutional networks. 248 13.5 Example: graph classification. 251 13.6 Inductive vs. transductive models. 252 13.7 Example: node
classification. 253 13.8 Layers for graph convolutional networks .256 13.9 Edge graphs.260 13.10 Summary. 261 14 Unsupervised learning 268 14.1 Taxonomy of unsupervised learning models.268 14.2 What makes a good generative model?.269 14.3 Quantifying performance. 271 14.4 Summary. 273 15 Generative Adversarial Networks 275
Contents X 15.1 15.2 15.3 15.4 15.5 15.6 15.7 Discrimination as a signal. 275 Improving stability.:.280 Progressive growing, minibatch discrimination, and truncation. 286 Conditional generation. . 288 Image translation. 290 StyleGAN . 295 Summary. 297 16 Normalizing flows 303 16.1 ID example. 303 16.2 General case. 306 16.3 Invertible network layers. 308 16.4 Multi-scale flows. 316 16.5 Applications. 317 16.6 Summary. 320 17 Variational autoencoders 326 17.1 Latent
variable models. 326 17.2 Nonlinear latent variable model. 327 17.3 Training. 330 17.4 ELBO properties . 333 17.5 Variational approximation. 335 17.6 The variational autoencoder. 335 17.7 The reparameterization trick. 338 17.8 Applications. ,. 339 17.9 Summary. 342 18 Diffusion models 348 18.1 Overview.348 18.2 Encoder (forward process). 349 18.3 Decoder model (reverse process). 355 18.4 Training. 356 18.5 Reparameterization of loss
function. 360 18.6 Implementation. 362 18.7 Summary. 367 19 Reinforcement learning 373 19.1 Markov decision processes, returns, and policies. 373 19.2 Expected return. 377 19.3 Tabular reinforcement learning. 381 19.4 Fitted Q-learning. 385 19.5 Policy gradient methods. 388 19.6 Actor-critic methods. 393 19.7 Offline reinforcement learning.394 19.8 Summary. 395
Contents 20 Why 20.1 20.2 20.3 20.4 20.5 20.6 20.7 xi does deep learning work? 401 The case against deep learning. 401 Factors that influence fitting performance . 402 Properties of loss functions. 406 Factors that determine generalization. 410 Do we need so many parameters? .·. 414 Do networks have to be deep?. 417 Summary. 418 21 Deep learning and ethics 420 21.1 Value alignment. 420 21.2 Intentional misuse. 426 21.3 Other social, ethical, and professional issues.428 21.4 Case study. 430 21.5 The value-free ideal of science. 431 21.6 Responsible AI research as a collective action problem. 432 21.7 Ways
forward. 433 21.8 Summary. 434 A В Notation 436 Mathematics 439 B.l Functions . 439 B.2 Binomial coefficients . 441 B.3 Vector, matrices, and tensors. 442 B.4 Special types of matrix. 445 B.5 Matrix calculus. 447 C Probability 448 C.l Random variables and probability distributions. 448 C.2 Expectation. 452 C.3 Normal probability distribution. 456 C.4 Sampling. 459 C.5 Distances between probability distributions. 459 Bibliography 462 Index 513 |
| adam_txt |
Contents xiii Preface Acknowledgements xv 1 Introduction 1.1 Supervised learning. 1.2 Unsupervised learning . 1.3 Reinforcement learning. 1.4 Ethics . 1.5 Structure of book. 1.6 Other books. 1.7 How to read this book. 1 1 7 11 12 15 15 16 2 Supervised learning 17 2.1 Supervised learning overview. 17 2.2 Linear regression example. 18 2.3 Summary. 22 3 Shallow neural networks 25 3.1 Neural network example. 25 3.2 Universal approximation theorem. 29 3.3 Multivariate inputs and outputs. 30 3.4 Shallow neural networks: general
case. 33 3.5 Terminology. 35 3.6 Summary. 36 4 Deep neural networks 41 4.1 Composing neural networks. 41 4.2 From composing networks to deep networks. 43 4.3 Deep neural networks. 45 4.4 Matrix notation. 48 4.5 Shallow vs. deep neural networks. 49 4.6 Summary. 52
viii Contents functions 56 Maximum likelihood . 56 Recipe for constructing loss functions. 60 Example 1: univariate regression . 61 Example 2: binary classification. 64 Example 3: multiclass classification. 67 Multiple outputs . 69 Cross-entropy loss. 71 Summary. 72 5 Loss 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 6 Fitting models 77 6.1 Gradient descent . 77 6.2 Stochastic gradient descent. 83 6.3 Momentum . 86 6.4 Adam. 88 6.5 Training algorithm hyperparameters . 91 6.6 Summary. 91 7 Gradients
and initialization 96 7.1 Problem definitions. 96 7.2 Computing derivatives. 97 7.3 Toy example. 100 7.4 Backpropagation algorithm. 103 7.5 Parameter initialization. . 107 7.6 Example training code. Ill 7.7 Summary. Ill 8 Measuring performance 118 8.1 Training a simple model.118 8.2 Sources of error. 120 8.3 Reducing error. 124 8.4 Double descent . 127 8.5 Choosing hyperparameters.132 8.6 Summary. 133 9 Regularization 138 9.1 Explicit
regularization . 138 9.2 Implicit regularization . 141 9.3 Heuristics to improve performance.144 9.4 Summary. 154 10 Convolutional networks 161 10.1 Invariance and equivariance.161 10.2 Convolutional networks for IDinputs. 163 10.3 Convolutional networks for 2Dinputs. 170
Contents 10.4 10.5 10.6 ix Downsampling and upsampling. 171 Applications. 174 Summary. 179 11 Residual networks 186 11.1 Sequential processing. 186 11.2 Residual connections and residual blocks. 189 11.3 Exploding gradients in residual networks. 192 11.4 Batch normalization . 192 11.5 Common residual architectures . 195 11.6 Why do nets with residual connections perform so well? .199 11.7 Summary.199 12 Transformers 207 12.1 Processing text data. 207 12.2 Dot-product self-attention.208 12.3 Extensions to dot-product self-attention . 213 12.4
Transformers. 215 12.5 Transformers for natural language processing. 216 12.6 Encoder model example: BERT. 219 12.7 Decoder model example: GPT3. 222 12.8 Encoder-decoder model example: machine translation. 226 12.9 Transformers for long sequences. 227 12.10 Transformers for images .228 12.11 Summary. 232 13 Graph neural networks 240 13.1 What is a graph?.240 13.2 Graph representation.243 13.3 Graph neural networks, tasks, and loss functions.245 13.4 Graph convolutional networks. 248 13.5 Example: graph classification. 251 13.6 Inductive vs. transductive models. 252 13.7 Example: node
classification. 253 13.8 Layers for graph convolutional networks .256 13.9 Edge graphs.260 13.10 Summary. 261 14 Unsupervised learning 268 14.1 Taxonomy of unsupervised learning models.268 14.2 What makes a good generative model?.269 14.3 Quantifying performance. 271 14.4 Summary. 273 15 Generative Adversarial Networks 275
Contents X 15.1 15.2 15.3 15.4 15.5 15.6 15.7 Discrimination as a signal. 275 Improving stability.:.280 Progressive growing, minibatch discrimination, and truncation. 286 Conditional generation. . 288 Image translation. 290 StyleGAN . 295 Summary. 297 16 Normalizing flows 303 16.1 ID example. 303 16.2 General case. 306 16.3 Invertible network layers. 308 16.4 Multi-scale flows. 316 16.5 Applications. 317 16.6 Summary. 320 17 Variational autoencoders 326 17.1 Latent
variable models. 326 17.2 Nonlinear latent variable model. 327 17.3 Training. 330 17.4 ELBO properties . 333 17.5 Variational approximation. 335 17.6 The variational autoencoder. 335 17.7 The reparameterization trick. 338 17.8 Applications. ,. 339 17.9 Summary. 342 18 Diffusion models 348 18.1 Overview.348 18.2 Encoder (forward process). 349 18.3 Decoder model (reverse process). 355 18.4 Training. 356 18.5 Reparameterization of loss
function. 360 18.6 Implementation. 362 18.7 Summary. 367 19 Reinforcement learning 373 19.1 Markov decision processes, returns, and policies. 373 19.2 Expected return. 377 19.3 Tabular reinforcement learning. 381 19.4 Fitted Q-learning. 385 19.5 Policy gradient methods. 388 19.6 Actor-critic methods. 393 19.7 Offline reinforcement learning.394 19.8 Summary. 395
Contents 20 Why 20.1 20.2 20.3 20.4 20.5 20.6 20.7 xi does deep learning work? 401 The case against deep learning. 401 Factors that influence fitting performance . 402 Properties of loss functions. 406 Factors that determine generalization. 410 Do we need so many parameters? .·. 414 Do networks have to be deep?. 417 Summary. 418 21 Deep learning and ethics 420 21.1 Value alignment. 420 21.2 Intentional misuse. 426 21.3 Other social, ethical, and professional issues.428 21.4 Case study. 430 21.5 The value-free ideal of science. 431 21.6 Responsible AI research as a collective action problem. 432 21.7 Ways
forward. 433 21.8 Summary. 434 A В Notation 436 Mathematics 439 B.l Functions . 439 B.2 Binomial coefficients . 441 B.3 Vector, matrices, and tensors. 442 B.4 Special types of matrix. 445 B.5 Matrix calculus. 447 C Probability 448 C.l Random variables and probability distributions. 448 C.2 Expectation. 452 C.3 Normal probability distribution. 456 C.4 Sampling. 459 C.5 Distances between probability distributions. 459 Bibliography 462 Index 513 |
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| spelling | Prince, Simon J. D. 1972- Verfasser (DE-588)1025919769 aut Understanding deep learning Simon J.D. Prince Cambridge, Massachusetts ; London, England The MIT Press [2023] © 2023 xi, 527 Seiten Illustrationen, Diagramme (teilweise farbig) txt rdacontent n rdamedia nc rdacarrier Literaturverzeichnis Seite 462-511 Deep Learning (DE-588)1135597375 gnd rswk-swf Deep Learning (DE-588)1135597375 s DE-604 Erscheint auch als Online-Ausgabe 978-0-262-04864-4 (DE-604)BV049467048 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034812866&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Prince, Simon J. D. 1972- Understanding deep learning Deep Learning (DE-588)1135597375 gnd |
| subject_GND | (DE-588)1135597375 |
| title | Understanding deep learning |
| title_auth | Understanding deep learning |
| title_exact_search | Understanding deep learning |
| title_exact_search_txtP | Understanding deep learning |
| title_full | Understanding deep learning Simon J.D. Prince |
| title_fullStr | Understanding deep learning Simon J.D. Prince |
| title_full_unstemmed | Understanding deep learning Simon J.D. Prince |
| title_short | Understanding deep learning |
| title_sort | understanding deep learning |
| topic | Deep Learning (DE-588)1135597375 gnd |
| topic_facet | Deep Learning |
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