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商品詳細
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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| 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.