國立高雄大學圖資館 |

Language: English

Back

Deep learning classifiers with memri...

James, Alex Pappachen.

Deep learning classifiers with memristive networkstheory and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning classifiers with memristive networksedited by Alex Pappachen James.
Reminder of title:	theory and applications /
other author:	James, Alex Pappachen.
Published:	Cham :Springer International Publishing :2020.
Description:	xiii, 213 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)
Online resource:	https://doi.org/10.1007/978-3-030-14524-8
ISBN:	9783030145248$q(electronic bk.)

Deep learning classifiers with memristive networkstheory and applications /
Deep learning classifiers with memristive networkstheory and applications /[electronic resource] :edited by Alex Pappachen James. - Cham :Springer International Publishing :2020. - xiii, 213 p. :ill. (some col.), digital ;24 cm. - Modeling and optimization in science and technologies,v.142196-7326 ;. - Modeling and optimization in science and technologies ;v.2..

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.

ISBN: 9783030145248$q(electronic bk.)

Standard No.: 10.1007/978-3-030-14524-8doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Deep learning classifiers with memristive networkstheory and applications /
LDR:01948nmm a2200325 a 4500 001 578121
003 DE-He213
005 20200207140026.0
006 m d
007 cr nn 008maaau
008 201208s2020 sz s 0 eng d
020 $a 9783030145248$q(electronic bk.)
020 $a 9783030145224$q(paper)
024 7 $a 10.1007/978-3-030-14524-8 $2 doi
035 $a 978-3-030-14524-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .D311 2020
245 0 0 $a Deep learning classifiers with memristive networks $h [electronic resource] : $b theory and applications / $c edited by Alex Pappachen James.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xiii, 213 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Modeling and optimization in science and technologies, $x 2196-7326 ; $v v.14
520 $a 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.
650 0 $a Neural networks (Computer science) $3 181982
650 0 $a Machine learning. $3 188639
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Image Processing and Computer Vision. $3 274051
700 1 $a James, Alex Pappachen. $3 866725
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Modeling and optimization in science and technologies ; $v v.2. $3 674885
856 4 0 $u https://doi.org/10.1007/978-3-030-14524-8
950 $a Intelligent Technologies and Robotics (Springer-42732)