Deep Neural Networks in a Mathematical Framework
Caterini, Anthony L.
Deep Neural Networks in a Mathematical Framework [electronic resource] / by Anthony L. Caterini, Dong Eui Chang. - 1st ed. 2018. - XIII, 84 p. online resource. - SpringerBriefs in Computer Science, 2191-5768 . - SpringerBriefs in Computer Science, .
Acceso multiusuario
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.
9783319753041
Artificial intelligence.
Pattern recognition.
Artificial Intelligence.
Pattern Recognition.
Q334-342
006.3
Deep Neural Networks in a Mathematical Framework [electronic resource] / by Anthony L. Caterini, Dong Eui Chang. - 1st ed. 2018. - XIII, 84 p. online resource. - SpringerBriefs in Computer Science, 2191-5768 . - SpringerBriefs in Computer Science, .
Acceso multiusuario
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.
9783319753041
Artificial intelligence.
Pattern recognition.
Artificial Intelligence.
Pattern Recognition.
Q334-342
006.3
