Computational methods for deep learning : theory, algorithms, and implementations / / Wei Qi Yan

• Record Nr.: UNISA996550556903316
• Autore: Yan Wei Qi
• Pubbl/distr/stampa: Singapore : , : Springer, , [2023]; ©2023
• ISBN: 981-9948-23-1
• Edizione: [Second edition.]
• Descrizione fisica: 1 online resource (235 pages)
• Collana: Texts in computer science
• Disciplina: 005.7
• Soggetti: Big data; Computer science; Data mining
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di contenuto: Intro -- 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.; 6 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.