In the last two decades deep learning has attracted a lot of attention internationally, solving problems in different application domains and achieving results beyond expectations. For example it has been applied in bioinformatics, game playing, imaging processing, object detection, robotic and drug discovery. One of the main reasons for the incremented use of deep learning algorithms is the need to implement approaches for the analysis of the large amount of data produces in every field, bringing researchers to dedicate their work to deep learning development. One of the main topics discussed up today is the possibility to run the training of deep models in a parallel fashion, so to reduce the time otherwise needed to find the hyperparameters and to make the achievement of the result faster.

Parallel Computing in Deep Learning: Bioinformatics Case Studiesa

D'Agostino D.;
2019

Abstract

In the last two decades deep learning has attracted a lot of attention internationally, solving problems in different application domains and achieving results beyond expectations. For example it has been applied in bioinformatics, game playing, imaging processing, object detection, robotic and drug discovery. One of the main reasons for the incremented use of deep learning algorithms is the need to implement approaches for the analysis of the large amount of data produces in every field, bringing researchers to dedicate their work to deep learning development. One of the main topics discussed up today is the possibility to run the training of deep models in a parallel fashion, so to reduce the time otherwise needed to find the hyperparameters and to make the achievement of the result faster.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1087341
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