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Deep neural networks in a mathematic...

Caterini, Anthony L.

Deep neural networks in a mathematical framework

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep neural networks in a mathematical frameworkby Anthony L. Caterini, Dong Eui Chang.
作者:	Caterini, Anthony L.
其他作者:	Chang, Dong Eui.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xiii, 84 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science)
電子資源:	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

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