Deep Learning Classifiers With Memristive Networks: Theory And Applications (Modeling And Optimization In Science And Technologies, 14)

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.

• | Author: Alex Pappachen James
• | Publisher: Springer
• | Publication Date: Apr 17, 2019
• | Number of Pages: 226 pages
• | Language: English
• | Binding: Hardcover
• | ISBN-10: 3030145220
• | ISBN-13: 9783030145224