|
" Deep learning classifiers with memristive networks : "
Alex Pappachen James, editor.
| Document Type
|
:
|
BL
|
| Record Number
|
:
|
861200
|
| Title & Author
|
:
|
Deep learning classifiers with memristive networks : : theory and applications /\ Alex Pappachen James, editor.
|
| Publication Statement
|
:
|
Cham, Switzerland :: Springer,, [2020].
|
| Series Statement
|
:
|
Modeling and optimization in science and technologies,; volume 14
|
| Page. NO
|
:
|
1 online resource (xiii, 213 pages) :: illustrations (some color).
|
| ISBN
|
:
|
3030145239
|
|
|
:
|
: 3030145247
|
|
|
:
|
: 9783030145231
|
|
|
:
|
: 9783030145248
|
|
|
:
|
3030145220
|
|
|
:
|
9783030145224
|
| Abstract
|
:
|
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.
|
| Subject
|
:
|
Machine learning.
|
| Subject
|
:
|
Neural networks (Computer science)
|
| Subject
|
:
|
Machine learning.
|
| Subject
|
:
|
Neural networks (Computer science)
|
| Dewey Classification
|
:
|
006.3/2
|
| LC Classification
|
:
|
QA76.87
|
| Added Entry
|
:
|
James, Alex Pappachen
|
| |