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Deep learning classifiers with memri...
~
James, Alex Pappachen.
Deep learning classifiers with memristive networkstheory and applications /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Deep learning classifiers with memristive networksedited by Alex Pappachen James.
Reminder of title:
theory and applications /
other author:
James, Alex Pappachen.
Published:
Cham :Springer International Publishing :2020.
Description:
xiii, 213 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Neural networks (Computer science)
Online resource:
https://doi.org/10.1007/978-3-030-14524-8
ISBN:
9783030145248$q(electronic bk.)
Deep learning classifiers with memristive networkstheory and applications /
Deep learning classifiers with memristive networks
theory and applications /[electronic resource] :edited by Alex Pappachen James. - Cham :Springer International Publishing :2020. - xiii, 213 p. :ill. (some col.), digital ;24 cm. - Modeling and optimization in science and technologies,v.142196-7326 ;. - Modeling and optimization in science and technologies ;v.2..
This book introduces readers to the fundamentals of deep neural network architectures, with a special emphasis on memristor circuits and systems. At first, the book offers an overview of neuro-memristive systems, including memristor devices, models, and theory, as well as an introduction to deep learning neural networks such as multi-layer networks, convolution neural networks, hierarchical temporal memory, and long short term memories, and deep neuro-fuzzy networks. It then focuses on the design of these neural networks using memristor crossbar architectures in detail. The book integrates the theory with various applications of neuro-memristive circuits and systems. It provides an introductory tutorial on a range of issues in the design, evaluation techniques, and implementations of different deep neural network architectures with memristors.
ISBN: 9783030145248$q(electronic bk.)
Standard No.: 10.1007/978-3-030-14524-8doiSubjects--Topical Terms:
181982
Neural networks (Computer science)
LC Class. No.: QA76.87
Dewey Class. No.: 006.32
Deep learning classifiers with memristive networkstheory and applications /
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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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Intelligent Technologies and Robotics (Springer-42732)
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EB QA76.87 .D311 2020 2020
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https://doi.org/10.1007/978-3-030-14524-8
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