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Deep neural networks in a mathematic...

Caterini, Anthony L.

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Deep neural networks in a mathematical framework

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep neural networks in a mathematical framework/ by 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

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

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

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Deep neural networks in a mathematical framework
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245 1 0 $a Deep neural networks in a mathematical framework $h [electronic resource] / $c by Anthony L. Caterini, Dong Eui Chang.
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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.
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