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Deep learning classifiers with memri...

James, Alex Pappachen.

Deep learning classifiers with memristive networkstheory and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep learning classifiers with memristive networksedited by Alex Pappachen James.
其他題名:	theory and applications /
其他作者:	James, Alex Pappachen.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xiii, 213 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science)
電子資源:	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 networkstheory 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

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