Computational Methods for Deep Learning: Theoretic, Practice and Applications 3030610802, 9783030610807
In this book, we work for the contents for knowledge transfer from the viewpoint of machine intelligence. We adopt the m
320 141 3MB
English Pages 130 [141] Year 2021
Table of contents :
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
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
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