Modeling and Optimization in Science and Technologies

Deep Learning Classifiers with Memristive Networks

Theory and Applications

Editors: James, Alex Pappachen (Ed.)

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  • Offers an introduction to deep neural network architectures
  • Describes in detail different kind of neuro-memristive systems, circuits and models
  • Shows how to implement different kind of neural networks in analog memristive circuits
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  • ISBN 978-3-030-14524-8
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About this book

This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.

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Table of contents (15 chapters)

Table of contents (15 chapters)
  • Introduction to Neuro-Memristive Systems

    Pages 3-12

    James, Alex Pappachen

  • Memristors: Properties, Models, Materials

    Pages 13-40

    Krestinskaya, Olga (et al.)

  • Deep Learning Theory Simplified

    Pages 41-55

    Bakambekova, Adilya (et al.)

  • Getting Started with TensorFlow Deep Learning

    Pages 57-67

    Toleubay, Yeldar (et al.)

  • Speech Recognition Application Using Deep Learning Neural Network

    Pages 69-79

    Izbassarova, Akzharkyn (et al.)

Buy this book

eBook n/a
  • ISBN 978-3-030-14524-8
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
Hardcover n/a
  • ISBN 978-3-030-14522-4
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Deep Learning Classifiers with Memristive Networks
Book Subtitle
Theory and Applications
Editors
  • Alex Pappachen James
Series Title
Modeling and Optimization in Science and Technologies
Series Volume
14
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-14524-8
DOI
10.1007/978-3-030-14524-8
Hardcover ISBN
978-3-030-14522-4
Series ISSN
2196-7326
Edition Number
1
Number of Pages
XIII, 213
Number of Illustrations
22 b/w illustrations, 102 illustrations in colour
Topics