Deep Neural Networks in a Mathematical Framework

ISBN-13: 9783319753034 / Angielski / Miękka / 2018 / 84 str.

Anthony Caterini; Dong Eui Chang

Opis

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