| Description |
1 online resource (xiii, 213 pages) : illustrations (some color) |
| Series |
Modeling and optimization in science and technologies, 2196-7326 ; volume 14 |
|
Modeling and optimization in science and technologies ; v. 14. 2196-7326
|
| 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 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 |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed April 23, 2019) |
| Subject |
Neural networks (Computer science)
|
|
Machine learning.
|
|
Machine learning
|
|
Neural networks (Computer science)
|
| Form |
Electronic book
|
| Author |
James, Alex Pappachen, editor
|
| ISBN |
9783030145248 |
|
3030145247 |
|
9783030145231 |
|
3030145239 |
|
