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Table of contents (6 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Deep Neural Networks in a Mathematical Framework
Authors: Anthony L. Caterini, Dong Eui Chang
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-3-319-75304-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2018
Softcover ISBN: 978-3-319-75303-4Published: 03 April 2018
eBook ISBN: 978-3-319-75304-1Published: 22 March 2018
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 84