Deep Neural Networks in a Mathematical Framework

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Artificial Intelligence, General Computing
Cover of the book Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini, Dong Eui Chang, Springer International Publishing
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Author: Anthony L. Caterini, Dong Eui Chang ISBN: 9783319753041
Publisher: Springer International Publishing Publication: March 22, 2018
Imprint: Springer Language: English
Author: Anthony L. Caterini, Dong Eui Chang
ISBN: 9783319753041
Publisher: Springer International Publishing
Publication: March 22, 2018
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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