| Document Type
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BL
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| Record Number
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890991
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| Main Entry
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Wani, M. A., (M. Arif)
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| Title & Author
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Advances in deep learning /\ M. Arif Wani, Farooq Ahmad Bhat, Saduf Afzal, Asif Iqbal Khan.
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| Publication Statement
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Singapore :: Springer,, [2020]
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| Series Statement
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Studies in big data ;; volume 57
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| Page. NO
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1 online resource
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| ISBN
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9789811367946
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: 9811367949
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9789811367939
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9811367930
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| Bibliographies/Indexes
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Includes bibliographical references.
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| Contents
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Intro; Preface; Contents; About the Authors; Abbreviations; 1 Introduction to Deep Learning; 1.1 Introduction; 1.2 Shallow Learning; 1.3 Deep Learning; 1.4 Why to Use Deep Learning; 1.5 How Deep Learning Works; 1.6 Deep Learning Challenges; Bibliography; 2 Basics of Supervised Deep Learning; 2.1 Introduction; 2.2 Convolutional Neural Network (ConvNet/CNN); 2.3 Evolution of Convolutional Neural Network Models; 2.4 Convolution Operation; 2.5 Architecture of CNN; 2.5.1 Convolution Layer; 2.5.2 Activation Function (ReLU); 2.5.3 Pooling Layer; 2.5.4 Fully Connected Layer; 2.5.5 Dropout
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2.6 Challenges and Future Research DirectionBibliography; 3 Training Supervised Deep Learning Networks; 3.1 Introduction; 3.2 Training Convolution Neural Networks; 3.3 Loss Functions and Softmax Classifier; 3.3.1 Mean Squared Error (L2) Loss; 3.3.2 Cross-Entropy Loss; 3.3.3 Softmax Classifier; 3.4 Gradient Descent-Based Optimization Techniques; 3.4.1 Gradient Descent Variants; 3.4.2 Improving Gradient Descent for Faster Convergence; 3.5 Challenges in Training Deep Networks; 3.5.1 Vanishing Gradient; 3.5.2 Training Data Size; 3.5.3 Overfitting and Underfitting; 3.5.4 High-Performance Hardware
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3.6 Weight Initialization Techniques3.6.1 Initialize All Weights to 0; 3.6.2 Random Initialization; 3.6.3 Random Weights from Probability Distribution; 3.6.4 Transfer Learning; 3.7 Challenges and Future Research Direction; Bibliography; 4 Supervised Deep Learning Architectures; 4.1 Introduction; 4.2 LeNet-5; 4.3 AlexNet; 4.4 ZFNet; 4.5 VGGNet; 4.6 GoogleNet; 4.7 ResNet; 4.8 Densely Connected Convolutional Network (DenseNet); 4.9 Capsule Network; 4.10 Challenges and Future Research Direction; Bibliography; 5 Unsupervised Deep Learning Architectures; 5.1 Introduction
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5.2 Restricted Boltzmann Machine (RBM)5.2.1 Variants of Restricted Boltzmann Machine; 5.3 Deep Belief Network; 5.3.1 Variants of Deep Belief Network; 5.4 Autoencoders; 5.4.1 Variations of Auto Encoders; 5.5 Deep Autoencoders; 5.6 Generative Adversarial Networks; 5.7 Challenges and Future Research Direction; Bibliography; 6 Supervised Deep Learning in Face Recognition; 6.1 Introduction; 6.2 Deep Learning Architectures for Face Recognition; 6.2.1 VGG-Face Architecture; 6.2.2 Modified VGG-Face Architecture; 6.3 Performance Comparison of Deep Learning Models for Face Recognition
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6.3.1 Performance Comparison with Variation in Facial Expression6.3.2 Performance Comparison on Images with Variation in Illumination Conditions; 6.3.3 Performance Comparison with Variation in Poses; 6.4 Challenges and Future Research Direction; Bibliography; 7 Supervised Deep Learning in Fingerprint Recognition; 7.1 Introduction; 7.2 Fingerprint Features; 7.3 Automatic Fingerprint Identification System (AFIS); 7.3.1 Feature Extraction Stage; 7.3.2 Minutia Matching Stage; 7.4 Deep Learning Architectures for Fingerprint Recognition; 7.4.1 Deep Learning for Fingerprint Segmentation
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| Abstract
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This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
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| Subject
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Education-- Data processing.
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Learning, Psychology of.
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Motivation in education.
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Education-- Data processing.
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EDUCATION-- Essays.
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EDUCATION-- Organizations Institutions.
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EDUCATION-- Reference.
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Learning, Psychology of.
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| Subject
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Motivation in education.
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| Dewey Classification
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370.15/23
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| LC Classification
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LB1065.W36 2020
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| Added Entry
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Afzal, Saduf
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Bhat, Farooq Ahmad
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Khan, Asif Iqbal
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