Könyv Deep Neural Networks in a Mathematical Framework Anthony Caterini

Deep Neural Networks in a Mathematical Framework

Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 10-18 napon belül
27 735 Ft
This SpringerBrief describes how to build a rigorous end-to-end mathematical framework for deep neur...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2018
oldal
84
EAN
9783319753034
ISBN
3319753037
Enbook ID
18802966
Súly
170
Méretek
234 x 156 x 14

Teljes leírás

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.

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