Computational methods for deep learning : theoretic, practice and applications

• Main Author: Yan, Wei Qi
• Format: eBook Book
• Language: English
• Published: Cham Springer 2021; Springer International Publishing AG; Springer International Publishing
• Edition: 1
• Series: Texts in Computer Science
• Subjects: Artificial Intelligence; Computer Imaging, Vision, Pattern Recognition and Graphics; Computer Science; Machine Learning; Mathematical Models of Cognitive Processes and Neural Networks; Mathematics of Computing
• ISBN: 9783030610807; 3030610802
• ISSN: 1868-0941; 1868-095X
• DOI: 10.1007/978-3-030-61081-4

Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence.

Table of Contents:

• Intro -- Preface -- Acknowledgements -- Contents -- About the Author -- Symbols and Acronyms -- 1 Introduction -- 1.1 Introduction -- 1.2 Deep Learning -- 1.3 The Chronicle of Deep Learning -- 1.4 Our Deep Learning Projects -- 1.5 Awarded Work in Deep Learning -- 1.6 Questions -- 2 Deep Learning Platforms -- 2.1 Introduction -- 2.2 MATLAB for Deep Learning -- 2.3 TensorFlow for Deep Learning -- 2.4 Data Augmentation -- 2.5 Fundamental Mathematics -- 2.6 Questions -- 3 CNN and RNN -- 3.1 CNN and YOLO -- 3.1.1 R-CNN -- 3.1.2 Mask R-CNN -- 3.1.3 YOLO -- 3.1.4 SSD -- 3.1.5 DenseNets and ResNets -- 3.2 RNN and Time Series Analysis -- 3.3 HMM -- 3.3.1 RNN: Recurrent Neural Networks -- 3.3.2 Time Series Analysis -- 3.4 Functional Spaces -- 3.4.1 Metric Space -- 3.5 Vector Space -- 3.5.1 Normed Space -- 3.5.2 Hilbert Space -- 3.6 Questions -- 4 Autoencoder and GAN -- 4.1 Autoencoder -- 4.2 Regularizations and Autoencoders -- 4.3 Generative Adversarial Networks -- 4.4 Information Theory -- 4.5 Questions -- 5 Reinforcement Learning -- 5.1 Introduction -- 5.2 Bellman Equation -- 5.3 Deep Q-Learning -- 5.4 Optimization -- 5.5 Data Fitting -- 5.6 Questions -- 6 CapsNet and Manifold Learning -- 6.1 CapsNet -- 6.2 Manifold Learning -- 6.3 Questions -- 7 Boltzmann Machines -- 7.1 Boltzmann Machine -- 7.2 Restricted Boltzmann Machine -- 7.3 Deep Boltzmann Machine -- 7.4 Probabilistic Graphical Models -- 7.5 Questions -- 8 Transfer Learning and Ensemble Learning -- 8.1 Transfer Learning -- 8.1.1 Transfer Learning -- 8.1.2 Taskonomy -- 8.2 Siamese Neural Networks -- 8.3 Ensemble Learning -- 8.4 Important Work in Deep Learning -- 8.5 Awarded Work in Deep Learning -- 8.6 Questions -- Glossary -- Index