Deep Neural Networks in a Mathematical Framework

Anthony L. Caterini

ISBN 10: 3319753037 / ISBN 13: 9783319753034

Edité par Springer, Berlin, Springer, 2018

A propos de cet article

Druck auf Anfrage Neuware - Printed after ordering - 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.

N° de réf. du vendeur 9783319753034

Détails bibliographiques

• Titre: Deep Neural Networks in a Mathematical Framework
• Éditeur: Springer, Berlin, Springer
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 3319753037
• ISBN à 13 chiffres: 9783319753034
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 234 L x 154 H x 6 P (en millimètres) 234 millimètres de largeur par 154 millimètres de hauteur par 6 millimètres de profondeur
• Poids: 184 grammes

A propos de ce titre

Synopsis

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.