國立虎尾科技大學 |

Deep Learning Classifiers with Memristive Networks = Theory and Applications /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep Learning Classifiers with Memristive Networks/ edited by Alex Pappachen James.
其他題名:	Theory and Applications /
其他作者:	James, Alex Pappachen.
面頁冊數:	XIII, 213 p. 124 illus., 102 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Image Processing and Computer Vision. -
電子資源:	https://doi.org/10.1007/978-3-030-14524-8
ISBN:	9783030145248

Deep Learning Classifiers with Memristive Networks = Theory and Applications /
Deep Learning Classifiers with Memristive NetworksTheory and Applications /[electronic resource] :edited by Alex Pappachen James. - 1st ed. 2020. - XIII, 213 p. 124 illus., 102 illus. in color.online resource. - Modeling and Optimization in Science and Technologies,142196-7326 ;. - Modeling and Optimization in Science and Technologies,4.

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

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

670819
Image Processing and Computer Vision.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Deep Learning Classifiers with Memristive Networks = Theory and Applications /
LDR:02296nam a22003855i 4500 001 1018077
003 DE-He213
005 20200705173204.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030145248 $9 978-3-030-14524-8
024 7 $a 10.1007/978-3-030-14524-8 $2 doi
035 $a 978-3-030-14524-8
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Deep Learning Classifiers with Memristive Networks $h [electronic resource] : $b Theory and Applications / $c edited by Alex Pappachen James.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XIII, 213 p. 124 illus., 102 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Modeling and Optimization in Science and Technologies, $x 2196-7326 ; $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 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Pattern Recognition. $3 669796
650 1 4 $a Computational Intelligence. $3 768837
650 0 $a Optical data processing. $3 639187
650 0 $a Data mining. $3 528622
650 0 $a Pattern recognition. $3 1253525
650 0 $a Computational intelligence. $3 568984
700 1 $a James, Alex Pappachen. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1289969
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030145224
776 0 8 $i Printed edition: $z 9783030145231
830 0 $a Modeling and Optimization in Science and Technologies, $x 2196-7326 ; $v 4 $3 1258709
856 4 0 $u https://doi.org/10.1007/978-3-030-14524-8
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)