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Learning representations: a deep architecture based approach

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Learning representations: a deep architecture based approach

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Castilla Escobar, J. (2014). Learning representations: a deep architecture based approach. http://hdl.handle.net/10251/45906.

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/45906

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Title: Learning representations: a deep architecture based approach
Author: Castilla Escobar, Joaquim
Director(s): Paredes Palacios, Roberto Albiol Colomer, Alberto
UPV Unit: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Read date / Event date:
2014-12-23
Issued date:
Abstract:
Representation Learning has become an active topic of research in the recent years. Neural models have been specially successful in this area due to recent developments in both algorithms and architectures. It is well ...[+]
Subjects: Representation learning , Restricted Boltzmann Machines , Convolutional Neural Networks , Gender recognition
Copyrigths: Cerrado
Publisher:
Universitat Politècnica de València
degree: Ingeniería Informática-Enginyeria Informàtica
Type: Proyecto/Trabajo fin de carrera/grado

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