Deep Learning: Concepts and Architectures

  • Witold Pedrycz
  • Shyi-Ming Chen

Part of the Studies in Computational Intelligence book series (SCI, volume 866)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Mohammad-Parsa Hosseini, Senbao Lu, Kavin Kamaraj, Alexander Slowikowski, Haygreev C. Venkatesh
    Pages 1-24
  3. Piyush Kaul, Brejesh Lall
    Pages 25-63
  4. Yuri Gordienko, Yuriy Kochura, Vlad Taran, Nikita Gordienko, Alexandr Rokovyi, Oleg Alienin et al.
    Pages 65-99
  5. Karishma Pawar, Vahida Z. Attar
    Pages 101-132
  6. Ahmad Asadi, Reza Safabakhsh
    Pages 133-167
  7. Wenwu Zhu, Xin Wang, Peng Cui
    Pages 169-210
  8. Ishan Jindal, Matthew Nokleby, Daniel Pressel, Xuewen Chen, Harpreet Singh
    Pages 211-235
  9. Mu-Yen Chen, Ting-Hsuan Chen, Shu-Hong Lin
    Pages 269-285
  10. Mihaela Maliţa, George Vlǎduţ Popescu, Gheorghe M. Ştefan
    Pages 287-319
  11. Anup Shrikant Kunte, Vahida Z. Attar
    Pages 321-339
  12. Back Matter
    Pages 341-342

About this book


This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.


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

Industry Sectors
Chemical Manufacturing
IT & Software
Consumer Packaged Goods
Materials & Steel
Finance, Business & Banking
Energy, Utilities & Environment
Oil, Gas & Geosciences