Deep Neural Networks in a Mathematical Framework - Couverture souple

Caterini, Anthony L. L.; Chang, Dong Eui

9783319753034: Deep Neural Networks in a Mathematical Framework

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ISBN 10 : 3319753037 ISBN 13 : 9783319753034

Editeur : Springer, 2018

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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.

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�diteur: Springer
Date d'�dition: 2018
Langue: anglais
ISBN 10: 3319753037
ISBN 13: 9783319753034
Reliure: Broch�
Num�ro d'�dition: 1
Nombre de pages: 100
Coordonn�es du fabricant: non disponible
Personne responsable: CMN Printpool Inh. Mathis Neumann
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Deep Neural Networks in a Mathematical Framework

Anthony L. Caterini|Dong Eui Chang

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ISBN 10 : 3319753037 ISBN 13 : 9783319753034

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Deep Neural Networks in a Mathematical Framework

Caterini, Anthony L.

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Deep Neural Networks in a Mathematical Framework (SpringerBriefs in Computer Science)

Caterini, Anthony L. L.; Chang, Dong Eui

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ISBN 10 : 3319753037 ISBN 13 : 9783319753034

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Deep Neural Networks in a Mathematical Framework

Anthony L. Caterini

Edit� par Springer, Berlin, Springer International Publishing, Springer, 2018

ISBN 10 : 3319753037 ISBN 13 : 9783319753034

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 84 pp. Englisch. N� de r�f. du vendeur 9783319753034

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Deep Neural Networks in a Mathematical Framework (SpringerBriefs in Computer Science)

Caterini, Anthony L. L.; Chang, Dong Eui

Edit� par Springer, 2018

ISBN 10 : 3319753037 ISBN 13 : 9783319753034

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Deep Neural Networks in a Mathematical Framework

Anthony L. Caterini

Edit� par Springer, Berlin, Springer International Publishing, Springer, 2018

ISBN 10 : 3319753037 ISBN 13 : 9783319753034

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Taschenbuch. Etat : Neu. 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 r�f. du vendeur 9783319753034

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