Deep Learning Classifiers with Memristive Networks: Theory and Applications

Author:   Alex Pappachen James
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   14
ISBN:  

9783030145224


Pages:   213
Publication Date:   17 April 2019
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Deep Learning Classifiers with Memristive Networks: Theory and Applications


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Overview

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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Author:   Alex Pappachen James
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   14
Weight:   0.512kg
ISBN:  

9783030145224


ISBN 10:   3030145220
Pages:   213
Publication Date:   17 April 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

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