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Deep Learning: Algorithms and Applications

  • Book
  • © 2020

Overview

Editors:
  1. Witold Pedrycz

    1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Shyi-Ming Chen

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

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • 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)

  1. Front Matter

    Pages i-xii
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  2. Activation Functions

    • Mohit Goyal, Rajan Goyal, P. Venkatappa Reddy, Brejesh Lall
    Pages 1-30

  3. Adversarial Examples in Deep Neural Networks: An Overview

    • Emilio Rafael Balda, Arash Behboodi, Rudolf Mathar
    Pages 31-65

  4. Representation Learning in Power Time Series Forecasting

    • Janosch Henze, Jens Schreiber, Bernhard Sick
    Pages 67-101

  5. Deep Learning Application: Load Forecasting in Big Data of Smart Grids

    • Abdulaziz Almalaq, Jun Jason Zhang
    Pages 103-128

  6. Fast and Accurate Seismic Tomography via Deep Learning

    • Mauricio Araya-Polo, Amir Adler, Stuart Farris, Joseph Jennings
    Pages 129-156

  7. Traffic Light and Vehicle Signal Recognition with High Dynamic Range Imaging and Deep Learning

    • Jian-Gang Wang, Lu-Bing Zhou
    Pages 157-192

  8. The Application of Deep Learning in Marine Sciences

    • Miguel Martin-Abadal, Ana Ruiz-Frau, Hilmar Hinz, Yolanda Gonzalez-Cid
    Pages 193-230

  9. Deep Learning Case Study on Imbalanced Training Data for Automatic Bird Identification

    • Juha Niemi, Juha T. Tanttu
    Pages 231-262

  10. Deep Learning for Person Re-identification in Surveillance Videos

    • Swathi Jamjala Narayanan, Boominathan Perumal, Sangeetha Saman, Aditya Pratap Singh
    Pages 263-297

  11. Deep Learning in Gait Analysis for Security and Healthcare

    • Omar Costilla-Reyes, Ruben Vera-Rodriguez, Abdullah S. Alharthi, Syed U. Yunas, Krikor B. Ozanyan
    Pages 299-334

  12. Deep Learning for Building Occupancy Estimation Using Environmental Sensors

    • Zhenghua Chen, Chaoyang Jiang, Mustafa K. Masood, Yeng Chai Soh, Min Wu, Xiaoli Li
    Pages 335-357

  13. Back Matter

    Pages 359-360
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Keywords

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.

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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