國立虎尾科技大學 |

Deep Neural Networks in a Mathematical Framework

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Deep Neural Networks in a Mathematical Framework/ by Anthony L. Caterini, Dong Eui Chang.
作者:	Caterini, Anthony L.
其他作者:	Chang, Dong Eui.
面頁冊數:	XIII, 84 p.online resource. :
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-75304-1
ISBN:	9783319753041

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. - 1st ed. 2018. - XIII, 84 p.online resource. - 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

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

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Deep Neural Networks in a Mathematical Framework
LDR:02371nam a22003855i 4500 001 997002
003 DE-He213
005 20200705125455.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319753041 $9 978-3-319-75304-1
024 7 $a 10.1007/978-3-319-75304-1 $2 doi
035 $a 978-3-319-75304-1
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Caterini, Anthony L. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1201846
245 1 0 $a Deep Neural Networks in a Mathematical Framework $h [electronic resource] / $c by Anthony L. Caterini, Dong Eui Chang.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XIII, 84 p. $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 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 Artificial intelligence. $3 559380
650 0 $a Pattern recognition. $3 1253525
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Pattern Recognition. $3 669796
700 1 $a Chang, Dong Eui. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1201847
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319753034
776 0 8 $i Printed edition: $z 9783319753058
830 0 $a SpringerBriefs in Computer Science, $x 2191-5768 $3 1255334
856 4 0 $u https://doi.org/10.1007/978-3-319-75304-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)