عنوان
پدید آورنده
موضوع
رده
LB1065 .W36 2020
LB1065
.
W36
2020
کتابخانه
محل استقرار
INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
9789811367946
(Number (ISBN
9811367949
Erroneous ISBN
9789811367939
Erroneous ISBN
9811367930
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Advances in deep learning /
General Material Designation
[Book]
First Statement of Responsibility
M. Arif Wani, Farooq Ahmad Bhat, Saduf Afzal, Asif Iqbal Khan.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Singapore :
Name of Publisher, Distributor, etc.
Springer,
Date of Publication, Distribution, etc.
[2020]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource
SERIES
Series Title
Studies in big data ;
Volume Designation
volume 57
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references.
CONTENTS NOTE
Text of Note
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
Text of Note
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
Text of Note
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
Text of Note
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
Text of Note
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
0
8
8
8
8
SUMMARY OR ABSTRACT
Text of Note
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.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Springer Nature
Stock Number
com.springer.onix.9789811367946
OTHER EDITION IN ANOTHER MEDIUM
Title
Advances in deep learning.
International Standard Book Number
9789811367939
TOPICAL NAME USED AS SUBJECT
Education-- Data processing.
Learning, Psychology of.
Motivation in education.
Education-- Data processing.
EDUCATION-- Essays.
EDUCATION-- Organizations & Institutions.
EDUCATION-- Reference.
Learning, Psychology of.
Motivation in education.
(SUBJECT CATEGORY (Provisional
EDU-- 024000
EDU-- 036000
EDU-- 042000
DEWEY DECIMAL CLASSIFICATION
Number
370
.
15/23
Edition
23
LIBRARY OF CONGRESS CLASSIFICATION
Class number
LB1065
Book number
.
W36
2020
PERSONAL NAME - PRIMARY RESPONSIBILITY
Wani, M. A., (M. Arif)
PERSONAL NAME - ALTERNATIVE RESPONSIBILITY
Afzal, Saduf
Bhat, Farooq Ahmad
Khan, Asif Iqbal
ORIGINATING SOURCE
Date of Transaction
20200823233100.0
Cataloguing Rules (Descriptive Conventions))
pn
ELECTRONIC LOCATION AND ACCESS
Electronic name
مطالعه متن کتاب [Book]
Y
