Deep Neural Networks in a Mathematical Framework
Deep Neural Networks in a Mathematical Framework by Anthony L. Caterini, Dong Eui Chang
English | 2018 | ISBN: 3319753037 | 84 Pages | PDF | 1.4 MB
English | 2018 | ISBN: 3319753037 | 84 Pages | PDF | 1.4 MB
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.