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Bibliografická citace

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BK
Cambridge, Massachusetts : The MIT Press, [2016]
xxii, 775 stran : ilustrace (některé barevné) ; 24 cm

objednat
ISBN 978-0-262-03561-3 (vázáno)
Adaptive computation and machine learning
Obsahuje bibliografii na stranách 711-766 a rejstřík
001461820
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 // CONTENTS // 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 // 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 // vi // CONTENTS // 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 // vii // CONTENTS // 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... 33Q // 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... 34g // 9.8 Efficient Convolution Algorithms... 35O // 9.9 Random or Unsupervised Features...35I // 9.10 The Neuroscientific Basis for Convolutional // Networks... 333 // 9.11 Convolutional Networks and the History of Deep Learning...359 // 10 Sequence Modeling: Recurrent and Recursive Nets 363 // 10.1 Unfolding Computational Graphs ...355 // 10.2 Recurrent Neural Networks...368 // 10.3 Bidirectional RNNs... 333 // 10.4 Encoder-Decoder Sequence-to-Sequence Architectures...385 // 10.5 Deep Recurrent Networks... 337 // 10.6 Recursive Neural Networks... 3gg // 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 // viii // CONTENTS // 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 PCA 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 // CONTENTS // 14.5 Denoising Autoencoders...501 // 14.6 Learning Manifolds with Autoencoders ...506 // 14.7 Contractive Autoencoders...5? // 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...535 // 15.5 Exponential Gains from Depth...543 // 15.6 Providing Clues to Discover Underlying Causes...544 // 16 Structured Probabilistic IVIodels for Deep Learning 549 // 16.1 The Challenge of Unstructured Modeling...55? // 16.2 Using Graphs to Describe Model Structure...554 // 16.3 Sampling from Graphical Models...57? // 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 584 // 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 // CONTENTS // 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 // XI

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