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Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini
Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini
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Deep Neural Networks in a Mathematical Framework

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Description

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 alsoto those outside of the neutral network community.
Release date Australia
April 3rd, 2018
Pages
84
Edition
1st ed. 2018
Audience
  • Professional & Vocational
Illustrations
XIII, 84 p.
ISBN-13
9783319753034
Product ID
27585914

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