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Deep Learning: Algorithms and Applications

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  • © 2020


  • 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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About this book

This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

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Table of contents (11 chapters)

Editors and Affiliations

  • Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Witold Pedrycz

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

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