Development and Analysis of Deep Learning Architectures

  • Witold Pedrycz
  • Shyi-Ming Chen

Part of the Studies in Computational Intelligence book series (SCI, volume 867)

Table of contents

  1. Front Matter
    Pages i-xi
  2. R. Krishnan, S. Jagannathan, V. A. Samaranayake
    Pages 1-29
  3. Salvatore Graziani, Maria Gabriella Xibilia
    Pages 31-59
  4. Mutlu Avci, Mehmet Sarıgül, Buse Melis Ozyildirim
    Pages 61-89
  5. Ankita Singh, Shayok Chakraborty
    Pages 91-115
  6. Omid Ghahabi, Pooyan Safari, Javier Hernando
    Pages 145-169
  7. Rami Cohen, Dima Ruinskiy, Janis Zickfeld, Hans IJzerman, Yizhar Lavner
    Pages 171-196
  8. Tugba Erpek, Timothy J. O’Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy
    Pages 223-266
  9. Andrew Johnston, Angjelo Marku
    Pages 267-289
  10. Back Matter
    Pages 291-292

About this book


This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.


Computational Intelligence Machine Learning Computer Vision Natural Language Processing Deep Learning Architectures Development Paradigms Pattern Recognition Image Processing Knowledge Representation AI

Editors and affiliations

  • Witold Pedrycz
    • 1
  • Shyi-Ming Chen
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of AlbertaEdmontonCanada
  2. 2.Department of Computer Science and Information EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan

Bibliographic information

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