Deep learning classifiers with memristive networks : theory and applications
著者
書誌事項
Deep learning classifiers with memristive networks : theory and applications
（Modeling and optimization in science and technologies / series editors Srikanta Patnaik, Ishwar K. Sethi, Xiaolong Li, v. 14）
Springer, c2020
大学図書館所蔵 全1件
注記
Includes bibliographical references
内容説明・目次
内容説明
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.
目次
Available in MS
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