Deep Learning Classifiers with Memristive Networks: Theory and ApplicationsКНИГИ » АППАРАТУРА
Название: Deep Learning Classifiers with Memristive Networks: Theory and Applications (Modeling and Optimization in Science and Technologies Book 14) Автор: Alex Pappachen James (Editor) Издательство: Springer Год: 2020 Формат: true pdf/epub Страниц: 216 Размер: 40.7 Mb Язык: English
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.
Neural Networks and Deep Learning: A Textbook Название: Neural Networks and Deep Learning: A Textbook Автор: Charu C. Aggarwal Издательство: Springer Год: 2018 Страниц: 497 Формат: PDF, EPUB...
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Deep Learning Classifiers with Memristive Networks: Theory and Applications 
