Neural networks and deep learning: a textbook
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| Sprache: | English |
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Cham, Switzerland
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[2023]
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| Ausgabe: | 2nd edition |
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| Beschreibung: | XXIX, 529 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm |
| ISBN: | 9783031296413 3031296419 |
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| 264 | 1 | |a Cham, Switzerland |b Springer |c [2023] | |
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Datensatz im Suchindex
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Contents 1 An Introduction to Neural Networks 1.1 Introduction. 1.2 Single Computational Layer: The Perceptron. 1.2.1 Use of Bias. 1.2.2 What Objective Function Is the Perceptron Optimizing? . 1.3 The Base Components of Neural Architectures . 1.3.1 Choice of Activation Function. 1.3.2 Softmax Activation Function . . 1.3.3 Common Loss Functions. 1.4 Multilayer Neural Networks. 1.4.1 The Multilayer Network as a Computational Graph. 1.5 The Importance of Nonlinearity . 1.5.1 Nonlinear Activations in Action. 1.6 Advanced Architectures and Structured Data. 1.7 Two Notable Benchmarks. 1.7.1 The MNIST Database of Handwritten Digits. 1.7.2 The ImageNet Database . 1.8
Summary. 1.9 Bibliographic Notes and Software Resources. 1.10 Exercises. 1 1 5 8 8 10 10 12 13 13 15 17 18 20 21 21 22 23 23 25 2 The Backpropagation Algorithm 2.1 Introduction. 2.2 The Computational Graph Abstraction. 2.2.1 Computational Graphs Create Complex Functions. 2.3 Backpropagation in Computational Graphs. 2.3.1 Computing Node-to-Node Derivatives with the Chain Rule . 2.3.2 Dynamic Programming for Computing Node-to-Node Derivatives. 2.3.3 Converting Node-to-Node Derivatives into Loss-to-Weight Derivatives. 42 29 29 30 31 33 34 38 XIII
CONTENTS XIV 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 Backpropagation in Neural Networks. 2.4.1 Some Useful Derivatives of Activation Functions. 2.4.2 Examples of Updates for Various Activations. The Vector-Centric View of Backpropagation. 2.5.1 Derivatives with Respect to Vectors. 2.5.2 Vector-Centric Chain Rule. 2.5.3 A Decoupled View of Vector-Centric Backpropagation. 2.5.4 Vector-Centric Backpropagation with Non-Layered Architectures. 57 The Not-So-Unimportant Details. 2.6.1 Mini-Batch Stochastic Gradient Descent . 2.6.2 Learning Rate Decay. 2.6.3 Checking the Correctness of Gradient Computation. 2.6.4 Regularization. 2.6.5 Loss Functions on Hidden Nodes. 2.6.6 Backpropagation Tricks for Handling Shared Weights. Tuning and Preprocessing . 2.7.1 Tuning
Hyperparameters. 2.7.2 Feature Preprocessing. 2.7.3 Initialization. Backpropagation Is Interpretable. Summary. Bibliographic Notes and Software Resources. Exercises. 3 Machine Learning with Shallow Neural Networks 3.1 3.2 3.3 3.4 3.5 Introduction. Neural Architectures for Binary Classification Models. 3.2.1 Revisiting the Perceptron. 3.2.2 Least-Squares Regression. 3.2.2.1 Widrow-Hoff Learning. 3.2.2.2 Closed Form Solutions. 3.2.3 Support Vector Machines. 3.2.4 Logistic Regression . 3.2.5 Comparison of Different
Models. Neural Architectures for Multiclass Models. 3.3.1 Multiclass Perceptron. 3.3.2 Weston-Watkins SVM. 3.3.3 Multinomial Logistic Regression (Softmax Classifier) . Unsupervised Learning with Autoencoders. 3.4.1 Linear Autoencoder with a Single Hidden Layer. 3.4.1.1 Connections with Singular Value Decomposition . 3.4.1.2 Sharing Weights in the Encoder and Decoder. 3.4.2 Nonlinear Activation Functions andDepth. 3.4.3 Application to Visualization. 3.4.4 Application to Outlier Detection. 3.4.5 Application to Multimodal Embeddings. 3.4.6 Benefits of Autoencoders. Recommender Systems . 44 46 48 50 51 51 52 58 58 60 60 61 61 62 62 63 64 66 67 67 68 68 73 73 75 75 76 78 79 79 81 82 84 84 85 86 88 89 91 91 92 93 95 95 96 96
CONTENTS 3.6 3.7 3.8 3.9 3.10 Text Embedding with Word2vec. 3.6.1 Neural Embedding with Continuous Bag of Words. 3.6.2 Neural Embedding with Skip-Gram Model. 3.6.3 Word2vec (SGNS) is Logistic Matrix Factorization. Simple Neural Architectures for Graph Embeddings. 3.7.1 Handling Arbitrary Edge Counts. 3.7.2 Beyond One-Hop Structural Models. 3.7.3 Multinomial Model . Summary. Bibliographic Notes and Software Resources. Exercises. 4 Deep Learning: Principles and Training Algorithms 4.1 4.2 4.3 4.4 4.5 4.6 4.7 Introduction. Why Is Depth Beneficial?. 4.2.1 Hierarchical Feature Engineering: How Depth Reveals Rich Structure. Why Is Training Deep Networks Hard?.
4.3.1 Geometric Understanding of the Effect of Gradient Ratios . . . 4.3.2 The Vanishing and Exploding Gradient Problems. 4.3.3 Cliffs and Valleys . 4.3.4 Convergence Problems with Depth. 4.3.5 Local Minima . Depth-Friendly Neural Architectures. 4.4.1 Activation Function Choice. 4.4.2 Dying Neurons and “Brain Damage” . 4.4.2.1 Leaky ReLU. 4.4.2.2 Maxout Networks . 4.4.3 Using Skip Connections. Depth-Friendly Gradient-Descent Strategies. 4.5.1 Importance of Preprocessing and Initialization. 4.5.2 Momentum-Based Learning. 4.5.3 Nesterov Momentum . 4.5.4 Parameter-Specific Learning Rates. 4.5.4.1 AdaGrad. 4.5.4.2
RMSProp. 4.5.4.3 AdaDelta. 4.5.5 Combining Parameter-Specific Learning and Momentum . 4.5.5.1 RMSProp with Nesterov Momentum. 4.5.5.2 Adam. 4.5.6 Gradient Clipping. 4.5.7 Polyak Averaging. Second-Order Derivatives: The Newton Method . 4.6.1 Example: Newton Method in the Quadratic Bowl . 4.6.2 Example: Newton Method in a Non-Quadratic Function. 4.6.3 The Saddle-Point Problem with Second-Order Methods. Fast Approximations of Newton Method. 4.7.1 Conjugate Gradient Method. 4.7.2 Quasi-Newton Methods and BFGS. XV 99 100 103 107 110 Ill 112 112 113 113 114 119 119 120 120 122 122 124 126 127 127 129 129 130 130 131 131 132 132 133 134 135 136 136 137 138 138 138 139 139 140 142 142 143 145 145 148
CONTENTS XVI 4.8 4.9 4.10 4.11 4.12 Batch Normalization. Practical Tricks for Acceleration and Compression. 4.9.1 GPU Acceleration. 4.9.2 Parallel and Distributed Implementations. 4.9.3 Algorithmic Tricks for Model Compression. Summary. Bibliographic Notes and Software Resources. Exercises. 5 Teaching Deep Learners to Generalize 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 Introduction. 5.1.1 Example: Linear Regression. 5.1.2 Example: Polynomial Regression. The Bias-Variance Trade-Off. Generalization Issues in Model Tuning and Evaluation. 5.3.1 Evaluating with Hold-Out and Cross-Validation. 5.3.2 Issues with Training at Scale.
5.3.3 How to Detect Need to Collect More Data. Penalty-Based Regularization . 5.4.1 Connections with Noise Injection. 5.4.2 Li-Regularization. 5.4.3 Li- or ^-Regularization?. 5.4.4 Penalizing Hidden Units: Learning Sparse Representations . . . Ensemble Methods. 5.5.1 Bagging and Subsampling. 5.5.2 Parametric Model Selection and Averaging. 5.5.3 Randomized Connection Dropping. 5.5.4 Dropout . 5.5.5 Data Perturbation Ensembles. Early Stopping. 5.6.1 Understanding Early Stopping from the Variance Perspective . . Unsupervised Pretraining. 5.7.1 Variations of Unsupervised Pretraining. 5.7.2 What About Supervised Pretraining?.
Continuation and Curriculum Learning. Parameter Sharing. Regularization in Unsupervised Applications. 5.10.1 When the Hidden Layer is Broader than the InputLayer . 5.10.1.1 Sparse Feature Learning. 5.10.2 Noise Injection: De-noising Autoencoders. 5.10.3 Gradient-Based Penalization: Contractive Autoencoders. 5.10.4 Hidden Probabilistic Structure: Variational Autoencoders . 5.10.4.1 Reconstruction and GenerativeSampling. 5.10.4.2 Conditional Variational Autoencoders. 5.10.4.3 Relationship with Generative Adversarial Networks . Summary. Bibliographic Notes and Software Resources. Exercises. 150 153 154 156 157 160 160 162 165 165 166 167 171 174 176 177 178 178 179 180 181 181 182 182 184 184 185 187 188 189 189 192 193 194 196 197 197 198 198 199 203 206 208 208 209 210 211
CONTENTS XVII 6 Radial Basis Function Networks 215 6.1 Introduction. 215 6.2 Training an RBF Network. 218 6.2.1 Training the Hidden Layer. 218 6.2.2 Training the Output Layer. 220 6.2.3 Iterative Construction of Hidden Layer. 221 6.2.4 Fully Supervised Learning of Hidden Layer. 222 6.3 Variations and Special Cases of RBF Networks. 223 6.3.1 Classification with Perceptron Criterion. 224 6.3.2 Classification with Hinge Loss. 224 6.3.3 Example of Linear Separability Promotedby RBF. 224 6.3.4 Application to Interpolation. 226 6.4 Relationship with Kernel Methods. 227 6.4.1 Kernel Regression Is a Special Case of RBF Networks. 227 6.4.2 Kernel SVM Is a Special Case of RBF Networks. 228 6.5 Summary. 229 6.6 Bibliographic Notes and Software
Resources. 229 6.7 Exercises. 229 7 Restricted Boltzmann Machines 231 7.1 Introduction. 231 7.2 Hopfield Networks. 232 7.2.1 Training a Hopfield Network. 235 7.2.2 Building a Toy Recommender and Its Limitations. 236 7.2.3 Increasing the Expressive Power of the Hopfield Network . 237 7.3 The Boltzmann Machine . 238 7.3.1 How a Boltzmann Machine Generates Data. 240 7.3.2 Learning the Weights of a Boltzmann Machine. 240 7.4 Restricted Boltzmann Machines . 242 7.4.1 Training the RBM. 244 7.4.2 Contrastive Divergence Algorithm. 245 7.5 Applications of Restricted Boltzmann Machines . 247 7.5.1 Dimensionality Reduction and Data Reconstruction. 247 7.5.2 RBMs for Collaborative Filtering . 249 7.5.3 Using RBMs for
Classification. 252 7.5.4 Topic Models with RBMs. 254 7.5.5 RBMs for Machine Learning with Multimodal Data. 256 7.6 Using RBMs beyond Binary Data Types . 258 7.7 Stacking Restricted Boltzmann Machines. 258 7.7.1 Unsupervised Learning. 261 7.7.2 Supervised Learning. 261 7.7.3 Deep Boltzmann Machines and Deep Belief Networks. 261 7.8 Summary. 262 7.9 Bibliographic Notes and Software Resources. 262 7.10 Exercises. 264
CONTENTS XVIII 8 Recurrent Neural Networks 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 9 Convolutional Neural Networks 9.1 9.2 9.3 9.4 265 Introduction. 265 The Architecture of Recurrent Neural Networks . 267 8.2.1 Language Modeling Example of RNN. 270 8.2.2 Backpropagation Through Time. 273 8.2.3 Bidirectional Recurrent Networks . 275 8.2.4 Multilayer Recurrent Networks. 277 The Challenges of Training Recurrent Networks . 278 8.3.1 Layer Normalization. 281 Echo-State Networks. 282 Long Short-Term Memory (LSTM) . 285 Gated Recurrent Units (GRUs). 287 Applications of Recurrent Neural Networks. 289 8.7.1 Contextualized Word Embeddings with ELMo. 290 8.7.2 Application to Automatic Image Captioning. 291 8.7.3 Sequence-to-Sequence Learning and Machine Translation . 292 8.7.4 Application to Sentence-
Level Classification. 295 8.7.5 Token-Level Classification with Linguistic Features . 296 8.7.6 Time-Series Forecasting and Prediction. 297 8.7.7 Temporal Recommender Systems . 299 8.7.8 Secondary Protein Structure Prediction. 301 8.7.9 End-to-End Speech Recognition. 301 8.7.10 Handwriting Recognition. 301 Summary. 302 Bibliographic Notes and Software Resources. 302 Exercises. 303 305 Introduction. 305 9.1.1 Historical Perspective and Biological Inspiration. 305 9.1.2 Broader Observations about Convolutional Neural Networks . . 306 The Basic Structure of a Convolutional Network. 307 9.2.1 Padding. 312 9.2.2 Strides. 313 9.2.3 The ReLU
Layer. 315 9.2.4 Pooling. 315 9.2.5 Fully Connected Layers. 317 9.2.6 The Interleaving between Layers. 317 9.2.7 Hierarchical Feature Engineering . 320 Training a Convolutional Network. 321 9.3.1 Backpropagating Through Convolutions . 321 9.3.2 Backpropagation as Convolution with Inverted/Transposed Filter. 322 9.3.3 Convolution/Backpropagation as Matrix Multiplications . 324 9.3.4 Data Augmentation. 326 Case Studies of Convolutional Architectures. 326 9.4.1 AlexNet. 327 9.4.2 ZFNet. 329 9.4.3 VGG. 330
CONTENTS 9.5 9.6 9.7 9.8 9.9 XIX 9.4.4 GoogLeNet. 333 9.4.5 ResNet. 335 9.4.6 Squeeze-and-Excitation Networks(SENets). 338 9.4.7 The Effects of Depth . 339 9.4.8 Pretrained Models. 340 Visualization and Unsupervised Learning. 341 9.5.1 Visualizing the Features of a Trained Network. 341 9.5.2 Convolutional Autoencoders. 347 Applications of Convolutional Networks. 351 9.6.1 Content-Based Image Retrieval. 352 9.6.2 Object Localization. 352 9.6.3 Object Detection. 354 9.6.4 Natural Language and SequenceLearningwith TextCNN . 355 9.6.5 Video Classification. 355 Summary. 356 Bibliographic Notes and Software
Resources. 356 Exercises. 359 10 Graph Neural Networks 361 10.1 Introduction. 361 10.2 Node Embeddings with Conventional Architectures. 362 10.2.1 Adjacency Matrix Representation and Feature Engineering . . . 364 10.3 Graph Neural Networks: The General Framework . 364 10.3.1 The Neighborhood Function . 368 10.3.2 Graph Convolution Function. 368 10.3.3 GraphSAGE. 369 10.3.4 Handling Edge Weights. 371 10.3.5 Handling New Vertices. 371 10.3.6 Handling Relational Networks. 372 10.3.7 Directed Graphs. 373 10.3.8 Gated Graph Neural Networks. 373 10.3.9 Comparison with Image Convolutional Networks. 374 10.4 Backpropagation in Graph Neural Networks
. 375 10.5 Beyond Nodes: Generating Graph-Level Models. 377 10.6 Applications of Graph'Neural Networks. 382 10.7 Summary. 384 10.8 Bibliographic Notes and Software Resources. 384 10.9 Exercises. 385 11 Deep 11.1 11.2 11.3 11.4 Reinforcement Learning 389 Introduction. 389 Stateless Algorithms: Multi-Armed Bandits. 391 The Basic Framework of Reinforcement Learning. 393 Monte Carlo Sampling. 395 11.4.1 Monte Carlo Sampling Algorithm. 395 11.4.2 Monte Carlo Rollouts with Function Approximators. 396
CONTENTS XX Bootstrapping for Value Function Learning. 398 11.5.1 Q-Learning. 399 11.5.2 Deep Learning Models as Function Approximators. 400 11.5.3 Example: Neural Network Specifics for VideoGame Setting . . . 403 11.5.4 On-Policy versus Off-Policy Methods: SARSA. 404 11.5.5 Modeling States versus State-Action Pairs . 405 11.6 Policy Gradient Methods. 407 11.6.1 Finite Difference Methods. 408 11.6.2 Likelihood Ratio Methods . 409 11.6.3 Actor-Critic Methods. 411 11.6.4 Continuous Action Spaces . 413 11.7 Monte Carlo Tree Search. 413 11.8 Case Studies. 415 11.8.1 AlphaGo and AlphaZero for Go and Chess. 415 11.8.2 Self-Learning Robots . 420 11.8.2.1 Deep Learning of Locomotion Skills. 420 11.8.2.2 Deep Learning of
Visuomotor Skills . 422 11.8.3 Building Conversational Systems: Deep Learning for Chatbots . 423 11.8.4 Self-Driving Cars . 425 11.8.5 Neural Architecture Search with Reinforcement Learning . 428 11.9 Practical Challenges Associated with Safety. 429 11.10 Summary. 429 11.11 Bibliographic Notes and Software Resources. 430 11.12 Exercises. 432 11.5 12 Advanced Topics in Deep Learning 435 12.1 Introduction. 435 12.2 Attention Mechanisms. 436 12.2.1 Recurrent Models of Visual Attention. 437 12.2.2 Attention Mechanisms for Image Captioning. 439 12.2.3 Soft Image Attention with Spatial Transformer. 440 12.2.4 Attention Mechanisms for Machine Translation. 442 12.2.5 Transformer Networks. 446 12.2.5.1 How Self Attention Helps. 446 12.2.5.2 The
Self-Attention Module. 447 12.2.5.3 Incorporating Positional Information. 449 12.2.5.4 The Sequence-to-Sequence Transformer. 450 12.2.5.5 Multihead Attention. 450 12.2.6 Transformer-Based Pre-trained Language Models . 451 12.2.6.1 GPT-n . 452 12.2.6.2 BERT. 454 12.2.6.3 T5. 455 12.2.7 Vision Transformer (ViT). 457 12.2.8 Attention Mechanisms in Graphs. 458 12.3 Neural Turing Machines. 459 12.4 Adversarial Deep Learning. 463 12.5 Generative Adversarial Networks (GANs). 467 12.5.1 Training a Generative Adversarial Network. 468 12.5.2 Comparison with Variational Autoencoder. 470
CONTENTS 12.5.3 Using GANs for Generating Image Data . 12.5.4 Conditional Generative Adversarial Networks. 12.6 Competitive Learning. 12.6.1 Vector Quantization. 12.6.2 Kohonen Self-Organizing Map. 12.7 Limitations of Neural Networks. 12.7.1 An Aspirational Goal: Few Shot Learning. 12.7.2 An Aspirational Goal: Energy-Efficient Learning. 12.8 Summary. 12.9 Bibliographic Notes and Software Resources. 12.10 Exercises. Correction to: Neural Networks and Deep Learning XXI 470 471 476 477 478 480 481 482 483 483 485 Cl Bibliography 487 Index 525 |
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Contents 1 An Introduction to Neural Networks 1.1 Introduction. 1.2 Single Computational Layer: The Perceptron. 1.2.1 Use of Bias. 1.2.2 What Objective Function Is the Perceptron Optimizing? . 1.3 The Base Components of Neural Architectures . 1.3.1 Choice of Activation Function. 1.3.2 Softmax Activation Function . . 1.3.3 Common Loss Functions. 1.4 Multilayer Neural Networks. 1.4.1 The Multilayer Network as a Computational Graph. 1.5 The Importance of Nonlinearity . 1.5.1 Nonlinear Activations in Action. 1.6 Advanced Architectures and Structured Data. 1.7 Two Notable Benchmarks. 1.7.1 The MNIST Database of Handwritten Digits. 1.7.2 The ImageNet Database . 1.8
Summary. 1.9 Bibliographic Notes and Software Resources. 1.10 Exercises. 1 1 5 8 8 10 10 12 13 13 15 17 18 20 21 21 22 23 23 25 2 The Backpropagation Algorithm 2.1 Introduction. 2.2 The Computational Graph Abstraction. 2.2.1 Computational Graphs Create Complex Functions. 2.3 Backpropagation in Computational Graphs. 2.3.1 Computing Node-to-Node Derivatives with the Chain Rule . 2.3.2 Dynamic Programming for Computing Node-to-Node Derivatives. 2.3.3 Converting Node-to-Node Derivatives into Loss-to-Weight Derivatives. 42 29 29 30 31 33 34 38 XIII
CONTENTS XIV 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 Backpropagation in Neural Networks. 2.4.1 Some Useful Derivatives of Activation Functions. 2.4.2 Examples of Updates for Various Activations. The Vector-Centric View of Backpropagation. 2.5.1 Derivatives with Respect to Vectors. 2.5.2 Vector-Centric Chain Rule. 2.5.3 A Decoupled View of Vector-Centric Backpropagation. 2.5.4 Vector-Centric Backpropagation with Non-Layered Architectures. 57 The Not-So-Unimportant Details. 2.6.1 Mini-Batch Stochastic Gradient Descent . 2.6.2 Learning Rate Decay. 2.6.3 Checking the Correctness of Gradient Computation. 2.6.4 Regularization. 2.6.5 Loss Functions on Hidden Nodes. 2.6.6 Backpropagation Tricks for Handling Shared Weights. Tuning and Preprocessing . 2.7.1 Tuning
Hyperparameters. 2.7.2 Feature Preprocessing. 2.7.3 Initialization. Backpropagation Is Interpretable. Summary. Bibliographic Notes and Software Resources. Exercises. 3 Machine Learning with Shallow Neural Networks 3.1 3.2 3.3 3.4 3.5 Introduction. Neural Architectures for Binary Classification Models. 3.2.1 Revisiting the Perceptron. 3.2.2 Least-Squares Regression. 3.2.2.1 Widrow-Hoff Learning. 3.2.2.2 Closed Form Solutions. 3.2.3 Support Vector Machines. 3.2.4 Logistic Regression . 3.2.5 Comparison of Different
Models. Neural Architectures for Multiclass Models. 3.3.1 Multiclass Perceptron. 3.3.2 Weston-Watkins SVM. 3.3.3 Multinomial Logistic Regression (Softmax Classifier) . Unsupervised Learning with Autoencoders. 3.4.1 Linear Autoencoder with a Single Hidden Layer. 3.4.1.1 Connections with Singular Value Decomposition . 3.4.1.2 Sharing Weights in the Encoder and Decoder. 3.4.2 Nonlinear Activation Functions andDepth. 3.4.3 Application to Visualization. 3.4.4 Application to Outlier Detection. 3.4.5 Application to Multimodal Embeddings. 3.4.6 Benefits of Autoencoders. Recommender Systems . 44 46 48 50 51 51 52 58 58 60 60 61 61 62 62 63 64 66 67 67 68 68 73 73 75 75 76 78 79 79 81 82 84 84 85 86 88 89 91 91 92 93 95 95 96 96
CONTENTS 3.6 3.7 3.8 3.9 3.10 Text Embedding with Word2vec. 3.6.1 Neural Embedding with Continuous Bag of Words. 3.6.2 Neural Embedding with Skip-Gram Model. 3.6.3 Word2vec (SGNS) is Logistic Matrix Factorization. Simple Neural Architectures for Graph Embeddings. 3.7.1 Handling Arbitrary Edge Counts. 3.7.2 Beyond One-Hop Structural Models. 3.7.3 Multinomial Model . Summary. Bibliographic Notes and Software Resources. Exercises. 4 Deep Learning: Principles and Training Algorithms 4.1 4.2 4.3 4.4 4.5 4.6 4.7 Introduction. Why Is Depth Beneficial?. 4.2.1 Hierarchical Feature Engineering: How Depth Reveals Rich Structure. Why Is Training Deep Networks Hard?.
4.3.1 Geometric Understanding of the Effect of Gradient Ratios . . . 4.3.2 The Vanishing and Exploding Gradient Problems. 4.3.3 Cliffs and Valleys . 4.3.4 Convergence Problems with Depth. 4.3.5 Local Minima . Depth-Friendly Neural Architectures. 4.4.1 Activation Function Choice. 4.4.2 Dying Neurons and “Brain Damage” . 4.4.2.1 Leaky ReLU. 4.4.2.2 Maxout Networks . 4.4.3 Using Skip Connections. Depth-Friendly Gradient-Descent Strategies. 4.5.1 Importance of Preprocessing and Initialization. 4.5.2 Momentum-Based Learning. 4.5.3 Nesterov Momentum . 4.5.4 Parameter-Specific Learning Rates. 4.5.4.1 AdaGrad. 4.5.4.2
RMSProp. 4.5.4.3 AdaDelta. 4.5.5 Combining Parameter-Specific Learning and Momentum . 4.5.5.1 RMSProp with Nesterov Momentum. 4.5.5.2 Adam. 4.5.6 Gradient Clipping. 4.5.7 Polyak Averaging. Second-Order Derivatives: The Newton Method . 4.6.1 Example: Newton Method in the Quadratic Bowl . 4.6.2 Example: Newton Method in a Non-Quadratic Function. 4.6.3 The Saddle-Point Problem with Second-Order Methods. Fast Approximations of Newton Method. 4.7.1 Conjugate Gradient Method. 4.7.2 Quasi-Newton Methods and BFGS. XV 99 100 103 107 110 Ill 112 112 113 113 114 119 119 120 120 122 122 124 126 127 127 129 129 130 130 131 131 132 132 133 134 135 136 136 137 138 138 138 139 139 140 142 142 143 145 145 148
CONTENTS XVI 4.8 4.9 4.10 4.11 4.12 Batch Normalization. Practical Tricks for Acceleration and Compression. 4.9.1 GPU Acceleration. 4.9.2 Parallel and Distributed Implementations. 4.9.3 Algorithmic Tricks for Model Compression. Summary. Bibliographic Notes and Software Resources. Exercises. 5 Teaching Deep Learners to Generalize 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9 5.10 5.11 5.12 5.13 Introduction. 5.1.1 Example: Linear Regression. 5.1.2 Example: Polynomial Regression. The Bias-Variance Trade-Off. Generalization Issues in Model Tuning and Evaluation. 5.3.1 Evaluating with Hold-Out and Cross-Validation. 5.3.2 Issues with Training at Scale.
5.3.3 How to Detect Need to Collect More Data. Penalty-Based Regularization . 5.4.1 Connections with Noise Injection. 5.4.2 Li-Regularization. 5.4.3 Li- or ^-Regularization?. 5.4.4 Penalizing Hidden Units: Learning Sparse Representations . . . Ensemble Methods. 5.5.1 Bagging and Subsampling. 5.5.2 Parametric Model Selection and Averaging. 5.5.3 Randomized Connection Dropping. 5.5.4 Dropout . 5.5.5 Data Perturbation Ensembles. Early Stopping. 5.6.1 Understanding Early Stopping from the Variance Perspective . . Unsupervised Pretraining. 5.7.1 Variations of Unsupervised Pretraining. 5.7.2 What About Supervised Pretraining?.
Continuation and Curriculum Learning. Parameter Sharing. Regularization in Unsupervised Applications. 5.10.1 When the Hidden Layer is Broader than the InputLayer . 5.10.1.1 Sparse Feature Learning. 5.10.2 Noise Injection: De-noising Autoencoders. 5.10.3 Gradient-Based Penalization: Contractive Autoencoders. 5.10.4 Hidden Probabilistic Structure: Variational Autoencoders . 5.10.4.1 Reconstruction and GenerativeSampling. 5.10.4.2 Conditional Variational Autoencoders. 5.10.4.3 Relationship with Generative Adversarial Networks . Summary. Bibliographic Notes and Software Resources. Exercises. 150 153 154 156 157 160 160 162 165 165 166 167 171 174 176 177 178 178 179 180 181 181 182 182 184 184 185 187 188 189 189 192 193 194 196 197 197 198 198 199 203 206 208 208 209 210 211
CONTENTS XVII 6 Radial Basis Function Networks 215 6.1 Introduction. 215 6.2 Training an RBF Network. 218 6.2.1 Training the Hidden Layer. 218 6.2.2 Training the Output Layer. 220 6.2.3 Iterative Construction of Hidden Layer. 221 6.2.4 Fully Supervised Learning of Hidden Layer. 222 6.3 Variations and Special Cases of RBF Networks. 223 6.3.1 Classification with Perceptron Criterion. 224 6.3.2 Classification with Hinge Loss. 224 6.3.3 Example of Linear Separability Promotedby RBF. 224 6.3.4 Application to Interpolation. 226 6.4 Relationship with Kernel Methods. 227 6.4.1 Kernel Regression Is a Special Case of RBF Networks. 227 6.4.2 Kernel SVM Is a Special Case of RBF Networks. 228 6.5 Summary. 229 6.6 Bibliographic Notes and Software
Resources. 229 6.7 Exercises. 229 7 Restricted Boltzmann Machines 231 7.1 Introduction. 231 7.2 Hopfield Networks. 232 7.2.1 Training a Hopfield Network. 235 7.2.2 Building a Toy Recommender and Its Limitations. 236 7.2.3 Increasing the Expressive Power of the Hopfield Network . 237 7.3 The Boltzmann Machine . 238 7.3.1 How a Boltzmann Machine Generates Data. 240 7.3.2 Learning the Weights of a Boltzmann Machine. 240 7.4 Restricted Boltzmann Machines . 242 7.4.1 Training the RBM. 244 7.4.2 Contrastive Divergence Algorithm. 245 7.5 Applications of Restricted Boltzmann Machines . 247 7.5.1 Dimensionality Reduction and Data Reconstruction. 247 7.5.2 RBMs for Collaborative Filtering . 249 7.5.3 Using RBMs for
Classification. 252 7.5.4 Topic Models with RBMs. 254 7.5.5 RBMs for Machine Learning with Multimodal Data. 256 7.6 Using RBMs beyond Binary Data Types . 258 7.7 Stacking Restricted Boltzmann Machines. 258 7.7.1 Unsupervised Learning. 261 7.7.2 Supervised Learning. 261 7.7.3 Deep Boltzmann Machines and Deep Belief Networks. 261 7.8 Summary. 262 7.9 Bibliographic Notes and Software Resources. 262 7.10 Exercises. 264
CONTENTS XVIII 8 Recurrent Neural Networks 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 9 Convolutional Neural Networks 9.1 9.2 9.3 9.4 265 Introduction. 265 The Architecture of Recurrent Neural Networks . 267 8.2.1 Language Modeling Example of RNN. 270 8.2.2 Backpropagation Through Time. 273 8.2.3 Bidirectional Recurrent Networks . 275 8.2.4 Multilayer Recurrent Networks. 277 The Challenges of Training Recurrent Networks . 278 8.3.1 Layer Normalization. 281 Echo-State Networks. 282 Long Short-Term Memory (LSTM) . 285 Gated Recurrent Units (GRUs). 287 Applications of Recurrent Neural Networks. 289 8.7.1 Contextualized Word Embeddings with ELMo. 290 8.7.2 Application to Automatic Image Captioning. 291 8.7.3 Sequence-to-Sequence Learning and Machine Translation . 292 8.7.4 Application to Sentence-
Level Classification. 295 8.7.5 Token-Level Classification with Linguistic Features . 296 8.7.6 Time-Series Forecasting and Prediction. 297 8.7.7 Temporal Recommender Systems . 299 8.7.8 Secondary Protein Structure Prediction. 301 8.7.9 End-to-End Speech Recognition. 301 8.7.10 Handwriting Recognition. 301 Summary. 302 Bibliographic Notes and Software Resources. 302 Exercises. 303 305 Introduction. 305 9.1.1 Historical Perspective and Biological Inspiration. 305 9.1.2 Broader Observations about Convolutional Neural Networks . . 306 The Basic Structure of a Convolutional Network. 307 9.2.1 Padding. 312 9.2.2 Strides. 313 9.2.3 The ReLU
Layer. 315 9.2.4 Pooling. 315 9.2.5 Fully Connected Layers. 317 9.2.6 The Interleaving between Layers. 317 9.2.7 Hierarchical Feature Engineering . 320 Training a Convolutional Network. 321 9.3.1 Backpropagating Through Convolutions . 321 9.3.2 Backpropagation as Convolution with Inverted/Transposed Filter. 322 9.3.3 Convolution/Backpropagation as Matrix Multiplications . 324 9.3.4 Data Augmentation. 326 Case Studies of Convolutional Architectures. 326 9.4.1 AlexNet. 327 9.4.2 ZFNet. 329 9.4.3 VGG. 330
CONTENTS 9.5 9.6 9.7 9.8 9.9 XIX 9.4.4 GoogLeNet. 333 9.4.5 ResNet. 335 9.4.6 Squeeze-and-Excitation Networks(SENets). 338 9.4.7 The Effects of Depth . 339 9.4.8 Pretrained Models. 340 Visualization and Unsupervised Learning. 341 9.5.1 Visualizing the Features of a Trained Network. 341 9.5.2 Convolutional Autoencoders. 347 Applications of Convolutional Networks. 351 9.6.1 Content-Based Image Retrieval. 352 9.6.2 Object Localization. 352 9.6.3 Object Detection. 354 9.6.4 Natural Language and SequenceLearningwith TextCNN . 355 9.6.5 Video Classification. 355 Summary. 356 Bibliographic Notes and Software
Resources. 356 Exercises. 359 10 Graph Neural Networks 361 10.1 Introduction. 361 10.2 Node Embeddings with Conventional Architectures. 362 10.2.1 Adjacency Matrix Representation and Feature Engineering . . . 364 10.3 Graph Neural Networks: The General Framework . 364 10.3.1 The Neighborhood Function . 368 10.3.2 Graph Convolution Function. 368 10.3.3 GraphSAGE. 369 10.3.4 Handling Edge Weights. 371 10.3.5 Handling New Vertices. 371 10.3.6 Handling Relational Networks. 372 10.3.7 Directed Graphs. 373 10.3.8 Gated Graph Neural Networks. 373 10.3.9 Comparison with Image Convolutional Networks. 374 10.4 Backpropagation in Graph Neural Networks
. 375 10.5 Beyond Nodes: Generating Graph-Level Models. 377 10.6 Applications of Graph'Neural Networks. 382 10.7 Summary. 384 10.8 Bibliographic Notes and Software Resources. 384 10.9 Exercises. 385 11 Deep 11.1 11.2 11.3 11.4 Reinforcement Learning 389 Introduction. 389 Stateless Algorithms: Multi-Armed Bandits. 391 The Basic Framework of Reinforcement Learning. 393 Monte Carlo Sampling. 395 11.4.1 Monte Carlo Sampling Algorithm. 395 11.4.2 Monte Carlo Rollouts with Function Approximators. 396
CONTENTS XX Bootstrapping for Value Function Learning. 398 11.5.1 Q-Learning. 399 11.5.2 Deep Learning Models as Function Approximators. 400 11.5.3 Example: Neural Network Specifics for VideoGame Setting . . . 403 11.5.4 On-Policy versus Off-Policy Methods: SARSA. 404 11.5.5 Modeling States versus State-Action Pairs . 405 11.6 Policy Gradient Methods. 407 11.6.1 Finite Difference Methods. 408 11.6.2 Likelihood Ratio Methods . 409 11.6.3 Actor-Critic Methods. 411 11.6.4 Continuous Action Spaces . 413 11.7 Monte Carlo Tree Search. 413 11.8 Case Studies. 415 11.8.1 AlphaGo and AlphaZero for Go and Chess. 415 11.8.2 Self-Learning Robots . 420 11.8.2.1 Deep Learning of Locomotion Skills. 420 11.8.2.2 Deep Learning of
Visuomotor Skills . 422 11.8.3 Building Conversational Systems: Deep Learning for Chatbots . 423 11.8.4 Self-Driving Cars . 425 11.8.5 Neural Architecture Search with Reinforcement Learning . 428 11.9 Practical Challenges Associated with Safety. 429 11.10 Summary. 429 11.11 Bibliographic Notes and Software Resources. 430 11.12 Exercises. 432 11.5 12 Advanced Topics in Deep Learning 435 12.1 Introduction. 435 12.2 Attention Mechanisms. 436 12.2.1 Recurrent Models of Visual Attention. 437 12.2.2 Attention Mechanisms for Image Captioning. 439 12.2.3 Soft Image Attention with Spatial Transformer. 440 12.2.4 Attention Mechanisms for Machine Translation. 442 12.2.5 Transformer Networks. 446 12.2.5.1 How Self Attention Helps. 446 12.2.5.2 The
Self-Attention Module. 447 12.2.5.3 Incorporating Positional Information. 449 12.2.5.4 The Sequence-to-Sequence Transformer. 450 12.2.5.5 Multihead Attention. 450 12.2.6 Transformer-Based Pre-trained Language Models . 451 12.2.6.1 GPT-n . 452 12.2.6.2 BERT. 454 12.2.6.3 T5. 455 12.2.7 Vision Transformer (ViT). 457 12.2.8 Attention Mechanisms in Graphs. 458 12.3 Neural Turing Machines. 459 12.4 Adversarial Deep Learning. 463 12.5 Generative Adversarial Networks (GANs). 467 12.5.1 Training a Generative Adversarial Network. 468 12.5.2 Comparison with Variational Autoencoder. 470
CONTENTS 12.5.3 Using GANs for Generating Image Data . 12.5.4 Conditional Generative Adversarial Networks. 12.6 Competitive Learning. 12.6.1 Vector Quantization. 12.6.2 Kohonen Self-Organizing Map. 12.7 Limitations of Neural Networks. 12.7.1 An Aspirational Goal: Few Shot Learning. 12.7.2 An Aspirational Goal: Energy-Efficient Learning. 12.8 Summary. 12.9 Bibliographic Notes and Software Resources. 12.10 Exercises. Correction to: Neural Networks and Deep Learning XXI 470 471 476 477 478 480 481 482 483 483 485 Cl Bibliography 487 Index 525 |
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| indexdate | 2025-02-13T09:00:48Z |
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| spelling | Aggarwal, Charu C. 1970- Verfasser (DE-588)133500101 aut Neural networks and deep learning a textbook Charu C. Aggarwal 2nd edition Cham, Switzerland Springer [2023] XXIX, 529 Seiten Illustrationen, Diagramme 23.5 cm x 15.5 cm txt rdacontent n rdamedia nc rdacarrier Deep Learning (DE-588)1135597375 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Neuronales Netz (DE-588)4226127-2 s Deep Learning (DE-588)1135597375 s DE-604 Maschinelles Lernen (DE-588)4193754-5 s Erscheint auch als Online-Ausgabe 978-3-031-29642-0 (DE-604)BV049033617 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034255298&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Aggarwal, Charu C. 1970- Neural networks and deep learning a textbook Deep Learning (DE-588)1135597375 gnd Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4226127-2 (DE-588)4193754-5 (DE-588)4123623-3 |
| title | Neural networks and deep learning a textbook |
| title_auth | Neural networks and deep learning a textbook |
| title_exact_search | Neural networks and deep learning a textbook |
| title_exact_search_txtP | Neural networks and deep learning a textbook |
| title_full | Neural networks and deep learning a textbook Charu C. Aggarwal |
| title_fullStr | Neural networks and deep learning a textbook Charu C. Aggarwal |
| title_full_unstemmed | Neural networks and deep learning a textbook Charu C. Aggarwal |
| title_short | Neural networks and deep learning |
| title_sort | neural networks and deep learning a textbook |
| title_sub | a textbook |
| topic | Deep Learning (DE-588)1135597375 gnd Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Deep Learning Neuronales Netz Maschinelles Lernen Lehrbuch |
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