Deep Learning Algorithms

✍ Scribed by Zoran Gacovski

• Publisher: Arcler Press
• Year: 2022
• Tongue: English
• Leaves: 412
• Category: Library

✦ Synopsis

This book covers different topics from deep learning algorithms, methods and approaches for deep learning, deep learning applications in biology, deep learning applications in medicine, and deep learning applications in pattern recognition systems. Section 1 focuses on methods and approaches for deep learning, describing advancements in deep learning theory and applications - perspective in 2020 and beyond; deep ensemble reinforcement learning with multiple deep deterministic policy gradient algorithm; dynamic decision-making for stabilized deep learning software platforms; deep learning for hyperspectral data classification through exponential momentum deep convolution neural networks; and ensemble network architecture for deep reinforcement learning. Section 2 focuses on deep learning applications in biology, describing fish detection using deep learning; deep learning identification of tomato leaf disease; deep learning for plant identification in natural environment; and applying deep learning models to mouse behavior recognition. Section 3 focuses on deep learning applications in medicine, describing application of deep learning in brain hemorrhage classification using transfer learning; a review of the application of deep learning in brachytherapy; exploring deep learning and transfer learning for colonic polyp classification; and deep learning algorithm for brain-computer interface. Section 4 focuses on deep learning applications in pattern recognition systems, describing application of deep learning in airport visibility forecast; hierarchical representations feature deep learning for face recognition; review of research on text sentiment analysis based on deep learning; classifying hand written digits with deep learning; and bitcoin price prediction based on deep learning methods.

✦ Table of Contents

Cover
Title Page
Copyright
DECLARATION
ABOUT THE EDITOR
TABLE OF CONTENTS
List of Contributors
List of Abbreviations
Preface
Section 1: Methods and Approaches for Deep Learning
Chapter 1 Advancements in Deep Learning Theory and Applications: Perspective in 2020 and Beyond
Abstract
Introduction
Deep Network Topologies
Application of Deep Learning
Modern Deep Learning Platforms
Training Algorithms
Routine Challenges of Deep Learning
Available Open-Source Datasets
References
Chapter 2 Deep Ensemble Reinforcement Learning With Multiple Deep Deterministic Policy Gradient Algorithm
Abstract
Introduction
Background
Methods
Results and Discussion
Conclusions
References
Chapter 3 Dynamic Decision-Making For Stabilized Deep Learning Software Platforms
Abstract
Introduction
Stabilized Control for Reliable Deep Learning Platforms
The Use of Lyapunov Optimization for Deep Learning Platforms
Emerging Applications
Conclusions
Acknowledgements
References
Chapter 4 Deep Learning For Hyperspectral Data Classification Through Exponential Momentum Deep Convolution Neural Networks
Abstract
Introduction
Feature Learning
Structure Design of Hyperspectral Data Classification Framework
Exponential Momentum Gradient Descent Algorithm
Experiment and Analysis
Conclusion
Acknowledgments
References
Chapter 5 Ensemble Network Architecture for Deep Reinforcement Learning
Abstract
Introduction
Related Work
Ensemble Methods for Deep Reinforcement Learning
Experiments
Conclusion
References
Section 2: Deep Learning Techniques Applied in Biology
Chapter 6 Fish Detection Using Deep Learning
Abstract
Introduction
Literature Review
Materials and Methods
Data Augmentation
Results and Discussion
Conclusion
Acknowledgments
References
Chapter 7 Can Deep Learning Identify Tomato Leaf Disease?
Abstract
Introduction
Related Work
Materials and Methods
Experiments and Results
Conclusion
Acknowledgments
References
Chapter 8 Deep Learning For Plant Identification In Natural Environment
Abstract
Introduction
Proposed Bjfu100 Dataset and Deep Learning Model
Experiments and Results
Resnet26 on Flavia Dataset
Conclusion
Acknowledgments
References
Chapter 9 Applying Deep Learning Models to Mouse Behavior Recognition
Abstract
Introduction
The Mouse Behavior Dataset
Experiments and Results
Conclusions
Acknowledgements
References
Section 3: Deep learning Applications in Medicine
Chapter 10 Application of Deep Learning in Neuroradiology: Brain Hemorrhage Classification Using Transfer Learning
Abstract
Introduction
Related Work
Convolutional Neural Network
Transfer Learning
Materials and Methods
Results and Discussion
Limitations
Conclusion
References
Chapter 11 A Review of the Application of Deep Learning in Brachytherapy
Abstract
Introduction
Organ Delineation and Segmentation
Segmentation and Reconstruction of the Applicator (Interstitial Needles)
Dose Calculation
Application of Treatment Planning System
Others
Conclusions
References
Chapter 12 Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification
Abstract
Introduction
Materials and Methods
Results and Discussion
Conclusion
Acknowledgments
References
Chapter 13 Deep Learning Algorithm For Brain-Computer Interface
Abstract
Introduction
Critical Review of the Related Literature
Comparison of Classification Algorithms
Discussion
Methodology
Conclusion
References
Section 4: Deep Learning in Pattern Recognition Tasks
Chapter 14 The Application of Deep Learning In Airport Visibility Forecast
Abstract
Introduction
Deep Learning
The Establishment of Prediction Model
Predictive Effect Test
Conclusions
References
Chapter 15 Hierarchical Representations Feature Deep Learning For Face Recognition
Abstract
Introduction
Images Preprocessing
Feature Extraction
Designing the Classifiers of Supervised Learning
Designing the Classifier Combining Unsupervised and Supervised Learning
Experiments
Conclusion
Acknowledgements
References
Chapter 16 Review of Research on Text Sentiment Analysis Based on Deep Learning
Abstract
Introduction
Brief Review on the Research Progress of Text Sentiment Analysis
Introduction to Text Sentiment Analysis Based on Deep Learning
Summary and Prospect
References
Chapter 17 Classifying Hand Written Digits With Deep Learning
Abstract
Introduction
Digit Classification with Deep Networks
Experiment
Conclusions
References
Chapter 18 Bitcoin Price Prediction Based on Deep Learning Methods
Abstract
Introduction
Dataset Exploration
Pre-Processing
Models
Results
Conclusion and Discussion
References
Index
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