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101 0 $aeng
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181   $ctxt$2rdacontent
182   $cc$2rdamedia
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200 10$aComputational methods for deep learning $etheory, algorithms, and implementations /$fWei Qi Yan
205   $aSecond edition.
210  1$aSingapore :$cSpringer,$d[2023]
210  4$d©2023
215   $a1 online resource (235 pages)
225 1 $aTexts in computer science
300   $aIncludes index.
311 08$aPrint version: Yan, Wei Qi Computational Methods for Deep Learning Singapore : Springer,c2023 9789819948222 
327   $aIntro -- Preface -- Acknowledgements -- Contents -- About the Author -- Acronyms -- Symbols -- 1 Introduction -- 1.1 Deep Learning as a Prominent Component of AI -- 1.2 Theory and Foundations of Deep Learning -- 1.3 The Chronicle of Deep Learning -- 1.4 Sample Projects for Deep Learning -- 1.5 The Databases for Deep Learning Projects -- 1.6 Awarded Papers on Deep Learning -- 1.7 Deep Learning Papers Published with Nature and Science -- 1.8 Organization of This Book -- 2 Deep Learning Platforms -- 2.1 Introduction -- 2.2 MATLAB for Deep Learning -- 2.3 TensorFlow for Deep Learning -- 2.4 Data Augmentation and Labeling -- 2.5 R for Deep Learning -- 2.6 Fundamental Mathematics -- Exercises -- 3 Convolutional Neural Networks and Recurrent Neural Networks -- 3.1 Multilayer Perceptron -- 3.2 Convolutional Neural Network and YOLO Models -- 3.2.1 Region-Based Convolutional Neural Network -- 3.2.2 Mask R-CNN -- 3.2.3 YOLO Models -- 3.2.4 Single Shot Multibox Detector -- 3.2.5 DenseNets and ResNets -- 3.2.6 Capsule Network -- 3.3 Recurrent Neural Networks and Time Series Analysis -- 3.3.1 Hidden Markov Model -- 3.3.2 Recurrent Neural Networks -- 3.3.3 Transformer Models -- 3.3.4 Generative Pre-trained Transformer Models -- 3.3.5 Time Series Analysis -- 3.4 Functional Analysis -- 3.4.1 Metric Space -- 3.4.2 Vector Space -- 3.4.3 Normed Space -- 3.4.4 Hilbert Space -- Exercises -- 4 Generative Adversarial Networks and Siamese Nets -- 4.1 Generative Adversarial Networks -- 4.2 Siamese Neural Networks -- 4.3 Autoencoder -- 4.4 Regularizations -- 4.5 Information Theory -- Exercises -- 5 Reinforcement Learning -- 5.1 Introduction -- 5.2 Bellman Equation -- 5.3 Deep Q-Learning -- 5.4 Control Theory -- 5.4.1 Mathematical Control Theory -- 5.4.2 Stochastic Control Theory -- 5.4.3 Fuzzy Control Theory -- 5.5 Optimization -- 5.6 Data Fitting -- 5.7 Polynomials.
327   $a6 Manifold Learning and Graph Neural Network -- 6.1 Manifold Learning -- 6.2 Probabilistic Graphical Models -- 6.3 Boltzmann Machine -- 6.4 Graph Neural Networks -- 6.4.1 Machine Learning on Graphs -- 6.4.2 Node Embeddings -- 6.4.3 Deep Graph Neural Networks -- 6.4.4 Graph Generating -- Exercises -- 7 Transfer Learning and Ensemble Learning -- 7.1 Transfer Learning -- 7.1.1 Concepts of Transfer Learning -- 7.1.2 Taskonomy -- 7.2 Ensemble Learning -- 7.3 Knowledge Distillation -- Glossary -- Names in This Book -- Index.
410  0$aTexts in computer science.
606   $aBig data
606   $aComputer science
606   $aData mining
615  0$aBig data.
615  0$aComputer science.
615  0$aData mining.
676   $a005.7
700   $aYan$b Wei Qi$0994732
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a996550556903316
996   $aComputational methods for deep learning$92814551
997   $aUNISA