Screen LibraryDeep learning classifiers with memristive networks : theory and applications /
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 lea...
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| Group Author: | |
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| Published: |
Springer,
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| Publisher Address: | Cham, Switzerland : |
| Publication Dates: | [2020] |
| Literature type: | Book |
| Language: | English |
| Series: |
Modeling and optimization in science and technologies,
volume 14 |
| Subjects: | |
| Summary: |
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 t |
| Carrier Form: | xiii, 213 pages : illustrations (some color) ; 24 cm. |
| Bibliography: | Includes bibliographical references. |
| ISBN: |
9783030145224 3030145220 |
| Index Number: | QA76 |
| CLC: |
TP181 TP183 |
| Call Number: | TP183/D311-3 |