Editors:
- Provides a comprehensive and up-to-date overview of deep learning by discussing a range of methodological and algorithmic issues
- Addresses implementations and case studies, identifying the best design practices and assessing business models and methodologies encountered in industry, health care, science, administration, and business
- Serves as a unique and well-structured reference resource for graduate and senior undergraduate students in areas such as computational intelligence, pattern recognition, computer vision, knowledge acquisition and representation, and knowledge-based systems
Part of the book series: Studies in Computational Intelligence (SCI, volume 865)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
About this book
Editors and Affiliations
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Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
Witold Pedrycz
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Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
Shyi-Ming Chen
Bibliographic Information
Book Title: Deep Learning: Algorithms and Applications
Editors: Witold Pedrycz, Shyi-Ming Chen
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-31760-7
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-31759-1Published: 04 November 2019
Softcover ISBN: 978-3-030-31762-1Published: 04 November 2020
eBook ISBN: 978-3-030-31760-7Published: 23 October 2019
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XII, 360
Number of Illustrations: 32 b/w illustrations, 139 illustrations in colour