Deep learning classifiers with memristive networks : theory and applications / Alex Pappachen James, editor.

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Deep learning classifiers with memristive networks : theory and applications / Alex Pappachen James, editor.

9783030145248 (electronic book)
3030145247 (electronic book)
9783030145224
3030145220

https://doi.org/10.1007/978-3-030-14524-8
https://doi.org/10.1007/978-3-030-14

Cham, Switzerland : Springer, [2020].

English

1 online resource (xiii, 213 pages) : illustrations.

10.1007/978-3-030-14524-8 doi
10.1007/978-3-030-14

QA76.87

006.3/2

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.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed April 23, 2019).

Modeling and optimization in science and technologies ; v. 14.

Print version: 9783030145224

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