Deep Neural Networks in a Mathematical Framework

Autor: Anthony Caterini, Dong Eui Chang

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Legare: Carte broșată

Editura: Springer International Publishing AG

Disponibilitate: În depozitul extern

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This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neur...

Informații despre carte

Autor

Anthony Caterini, Dong Eui Chang

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2018

Pagini

EAN

9783319753034

ISBN

3319753037

Enbook ID

18802966

Editura

Springer International Publishing AG

Greutate

170

Dimensiuni

234 x 156 x 14

Descriere completă

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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Deep Neural Networks in a Mathematical Framework

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Descriere completă

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