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Deep Learning: Concepts and Architectures

  • 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

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    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 866)

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Table of contents (11 chapters)

  1. Front Matter

    Pages i-xii
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  2. Deep Learning Architectures

    • Mohammad-Parsa Hosseini, Senbao Lu, Kavin Kamaraj, Alexander Slowikowski, Haygreev C. Venkatesh
    Pages 1-24

  3. Theoretical Characterization of Deep Neural Networks

    • Piyush Kaul, Brejesh Lall
    Pages 25-63

  4. Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models

    • Yuri Gordienko, Yuriy Kochura, Vlad Taran, Nikita Gordienko, Alexandr Rokovyi, Oleg Alienin et al.
    Pages 65-99

  5. Assessment of Autoencoder Architectures for Data Representation

    • Karishma Pawar, Vahida Z. Attar
    Pages 101-132

  6. The Encoder-Decoder Framework and Its Applications

    • Ahmad Asadi, Reza Safabakhsh
    Pages 133-167

  7. Deep Learning for Learning Graph Representations

    • Wenwu Zhu, Xin Wang, Peng Cui
    Pages 169-210

  8. Deep Neural Networks for Corrupted Labels

    • Ishan Jindal, Matthew Nokleby, Daniel Pressel, Xuewen Chen, Harpreet Singh
    Pages 211-235

  9. Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task

    • Yalong Jiang, Zheru Chi
    Pages 237-268

  10. Using Convolutional Neural Networks to Forecast Sporting Event Results

    • Mu-Yen Chen, Ting-Hsuan Chen, Shu-Hong Lin
    Pages 269-285

  11. Heterogeneous Computing System for Deep Learning

    • Mihaela MaliÅ£a, George VlÇŽduÅ£ Popescu, Gheorghe M. Åžtefan
    Pages 287-319

  12. Progress in Neural Network Based Statistical Language Modeling

    • Anup Shrikant Kunte, Vahida Z. Attar
    Pages 321-339

  13. Back Matter

    Pages 341-342
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Keywords

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.

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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