Deep Neural Networks in a Mathematical Framework (Paperback, 1st ed. 2018)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computer Science
Release date:	March 2018
First published:	2018
Authors:	Anthony L. Caterini • Dong Eui Chang
Dimensions:	235 x 155 x 14mm (L x W x T)
Format:	Paperback
Pages:	84
Edition:	1st ed. 2018
ISBN-13:	978-3-319-75303-4
Categories:	Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	3-319-75303-7
Barcode:	9783319753034

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