株式会社極東書店トップ商品一覧Deep Learning Classifiers with Memristive Networks: Theory and Applications. 2020 ed.

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Deep Learning Classifiers with Memristive Networks: Theory and Applications. 2020 ed.

Deep Learning Classifiers with Memristive Networks: Theory and Applications. 2020 ed.

・ISBN 978-3-030-14522-4 hard EUR 169.99

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お気に入り
著者・編者James, Alex Pappachen (ed.),
シリーズ (Modeling and Optimization in Science and Technologies)
出版社 (Springer Nature Switzerland AG, SZ)
出版年月2019
ページ数213 pp.
言語ENG
ニュース番号<A03-92595>

解説

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.