國立高雄大學圖資館 |

Language: English

Back

Deep neural networks in a mathematic...

Caterini, Anthony L.

Deep neural networks in a mathematical framework

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep neural networks in a mathematical frameworkby Anthony L. Caterini, Dong Eui Chang.
Author:	Caterini, Anthony L.
other author:	Chang, Dong Eui.
Published:	Cham :Springer International Publishing :2018.
Description:	xiii, 84 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Neural networks (Computer science)
Online resource:	http://dx.doi.org/10.1007/978-3-319-75304-1
ISBN:	9783319753041$q(electronic bk.)

Deep neural networks in a mathematical framework
Caterini, Anthony L.

Deep neural networks in a mathematical framework[electronic resource] /by Anthony L. Caterini, Dong Eui Chang. - Cham :Springer International Publishing :2018. - xiii, 84 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.

ISBN: 9783319753041$q(electronic bk.)

Standard No.: 10.1007/978-3-319-75304-1doiSubjects--Topical Terms:

181982
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Deep neural networks in a mathematical framework
LDR:02051nmm a2200325 a 4500 001 533426
003 DE-He213
005 20180322112741.0
006 m d
007 cr nn 008maaau
008 181205s2018 gw s 0 eng d
020 $a 9783319753041$q(electronic bk.)
020 $a 9783319753034$q(paper)
024 7 $a 10.1007/978-3-319-75304-1 $2 doi
035 $a 978-3-319-75304-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.87
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.32 $2 23
090 $a QA76.87 $b .C359 2018
100 1 $a Caterini, Anthony L. $3 809113
245 1 0 $a Deep neural networks in a mathematical framework $h [electronic resource] / $c by Anthony L. Caterini, Dong Eui Chang.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xiii, 84 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
520 $a This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks. This SpringerBrief is one step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks.This SpringerBrief is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.
650 0 $a Neural networks (Computer science) $3 181982
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 252959
650 2 4 $a Pattern Recognition. $3 273706
700 1 $a Chang, Dong Eui. $3 719745
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in computer science. $3 559641
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-75304-1
950 $a Computer Science (Springer-11645)