Learning Deep Learning
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista Brasileira de Ensino de Física (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-11172022000100725 |
Resumo: | As a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand and apply deep learning, it is important to become familiarized with the respective basic concepts. In this text, after briefly revising some works relating to physics and deep learning, we introduce and discuss some of the main principles of deep learning as well as some of its principal models. More specifically, we describe the main elements, their use, as well as several of the possible network architectures. A companion tutorial in Python has been prepared in order to complement our approach. |
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Learning Deep LearningDeep learningTutorialClassificationAs a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand and apply deep learning, it is important to become familiarized with the respective basic concepts. In this text, after briefly revising some works relating to physics and deep learning, we introduce and discuss some of the main principles of deep learning as well as some of its principal models. More specifically, we describe the main elements, their use, as well as several of the possible network architectures. A companion tutorial in Python has been prepared in order to complement our approach.Sociedade Brasileira de Física2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-11172022000100725Revista Brasileira de Ensino de Física v.44 2022reponame:Revista Brasileira de Ensino de Física (Online)instname:Sociedade Brasileira de Física (SBF)instacron:SBF10.1590/1806-9126-rbef-2022-0101info:eu-repo/semantics/openAccessArruda,Henrique F. deBenatti,AlexandreComin,César HenriqueCosta,Luciano da F.eng2022-08-26T00:00:00Zoai:scielo:S1806-11172022000100725Revistahttp://www.sbfisica.org.br/rbef/https://old.scielo.br/oai/scielo-oai.php||marcio@sbfisica.org.br1806-91261806-1117opendoar:2022-08-26T00:00Revista Brasileira de Ensino de Física (Online) - Sociedade Brasileira de Física (SBF)false |
dc.title.none.fl_str_mv |
Learning Deep Learning |
title |
Learning Deep Learning |
spellingShingle |
Learning Deep Learning Arruda,Henrique F. de Deep learning Tutorial Classification |
title_short |
Learning Deep Learning |
title_full |
Learning Deep Learning |
title_fullStr |
Learning Deep Learning |
title_full_unstemmed |
Learning Deep Learning |
title_sort |
Learning Deep Learning |
author |
Arruda,Henrique F. de |
author_facet |
Arruda,Henrique F. de Benatti,Alexandre Comin,César Henrique Costa,Luciano da F. |
author_role |
author |
author2 |
Benatti,Alexandre Comin,César Henrique Costa,Luciano da F. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Arruda,Henrique F. de Benatti,Alexandre Comin,César Henrique Costa,Luciano da F. |
dc.subject.por.fl_str_mv |
Deep learning Tutorial Classification |
topic |
Deep learning Tutorial Classification |
description |
As a consequence of its capability of creating high level abstractions from data, deep learning has been effectively employed in a wide range of applications, including physics. Though deep learning can be, at first and simplistically understood in terms of very large neural networks, it also encompasses new concepts and methods. In order to understand and apply deep learning, it is important to become familiarized with the respective basic concepts. In this text, after briefly revising some works relating to physics and deep learning, we introduce and discuss some of the main principles of deep learning as well as some of its principal models. More specifically, we describe the main elements, their use, as well as several of the possible network architectures. A companion tutorial in Python has been prepared in order to complement our approach. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-11172022000100725 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-11172022000100725 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-9126-rbef-2022-0101 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Física |
publisher.none.fl_str_mv |
Sociedade Brasileira de Física |
dc.source.none.fl_str_mv |
Revista Brasileira de Ensino de Física v.44 2022 reponame:Revista Brasileira de Ensino de Física (Online) instname:Sociedade Brasileira de Física (SBF) instacron:SBF |
instname_str |
Sociedade Brasileira de Física (SBF) |
instacron_str |
SBF |
institution |
SBF |
reponame_str |
Revista Brasileira de Ensino de Física (Online) |
collection |
Revista Brasileira de Ensino de Física (Online) |
repository.name.fl_str_mv |
Revista Brasileira de Ensino de Física (Online) - Sociedade Brasileira de Física (SBF) |
repository.mail.fl_str_mv |
||marcio@sbfisica.org.br |
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1752122426209075200 |