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Studies in Big Data

Advances in Deep Learning

Authors: Wani, M.A., Bhat, F.A., Afzal, S., Khan, A.I.

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  • Discusses a contemporary research area, i.e. deep learning
  • Elaborates on both basic and advanced concepts in deep learning
  • Illustrates several advanced concepts like classification, face recognition, and fingerprint recognition
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書籍の購入

イーブック ¥12,354
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-981-13-6794-6
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: EPUB, PDF
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ハードカバー ¥21,059
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-981-13-6793-9
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
この書籍について

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.

著者について

Prof. M. Arif Wani completed his M.Tech. in Computer Technology at the Indian Institute of Technology, Delhi and his PhD in Computer Vision at Cardiff University, UK. Currently, he is a Professor at the University of Kashmir, having previously served as a Professor at California State University Bakersfield. His main research interests are in gene expression datasets, face recognition techniques/algorithms, artificial neural networks and deep architectures. He has published many papers in reputed journals and conferences in these areas. He was honored with The International Technology Institute Award in 2002 by the International Technology Institute, California, USA. He is a member of many academic and professional bodies, e.g. the Indian Society for Technical Education, Computer Society of India, IEEE USA and Optical Society of America.

Dr. Farooq Ahmad Bhat completed his MPhil and PhD in Computer Science at the University of Kashmir. His dissertation focused on ‘Efficient and robust convolutional neural network based models for face recognition’. Currently, his main interests are in artificial intelligence, machine learning and deep learning, areas in which he has published many articles.

Dr. Saduf Afzal teaches at the Islamic University of Science and Technology, Kashmir, India. She completed her BCA, MCA, MPhil and PhD at the Department of Computer Science, University of Kashmir. She has also worked as an academic counselor for the MCA program at IGNOU University. Her main research interests are in machine learning, deep learning and neural networks. She has published many articles in high-impact journals and conference proceedings.

Dr. Asif Iqbal Khan currently works as a Lecturer in the Higher Education Department, Kashmir, India. He completed his MCA, MPhil and PhD at the Department of Computer Science, University of Kashmir. His main research interests are in machine learning, deep learning, and image processing. He is actively publishing in these areas.


Table of contents (8 chapters)

Table of contents (8 chapters)

書籍の購入

イーブック ¥12,354
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-981-13-6794-6
  • ウォーターマーク付、 DRMフリー
  • ファイル形式: EPUB, PDF
  • どの電子書籍リーダーからでもすぐにお読みいただけます。
  • ご購入後、すぐにダウンロードしていただけます。
ハードカバー ¥21,059
価格の適用国: Japan (日本円価格は個人のお客様のみ有効) (小計)
  • ISBN 978-981-13-6793-9
  • 個人のお客様には、世界中どこでも配送料無料でお届けします。
  • Usually dispatched within 3 to 5 business days.
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書誌情報

Bibliographic Information
Book Title
Advances in Deep Learning
Authors
Series Title
Studies in Big Data
Series Volume
57
Copyright
2020
Publisher
Springer Singapore
Copyright Holder
Springer Nature Singapore Pte Ltd.
イーブック ISBN
978-981-13-6794-6
DOI
10.1007/978-981-13-6794-6
ハードカバー ISBN
978-981-13-6793-9
Series ISSN
2197-6503
Edition Number
1
Number of Pages
XIV, 149
Number of Illustrations
34 b/w illustrations, 53 illustrations in colour
Topics