Matches in SemOpenAlex for { <https://semopenalex.org/work/W2583148087> ?p ?o ?g. }
Showing items 1 to 82 of
82
with 100 items per page.
- W2583148087 endingPage "56" @default.
- W2583148087 startingPage "50" @default.
- W2583148087 abstract "Big Data concerns with large-volume complex growing data. Given the fast development of data storage and network, organizations are collecting large ever-growing datasets that can have useful information. In order to extract information from these datasets within useful time, it is important to use distributed and parallel algorithms. One common usage of big data is machine learning, in which collected data is used to predict future behavior. Deep-Learning using Artificial Neural Networks is one of the popular methods for extracting information from complex datasets. Deep-learning is capable of more creating complex models than traditional probabilistic machine learning techniques. This work presents a step-by-step guide on how to prototype a Deep-Learning application that executes both on GPU and CPU clusters. Python and Redis are the core supporting tools of this guide. This tutorial will allow the reader to understand the basics of building a distributed high performance GPU application in a few hours. Since we do not depend on any deep-learning application or framework—we use low-level building blocks—this tutorial can be adjusted for any other parallel algorithm the reader might want to prototype on Big Data. Finally, we will discuss how to move from a prototype to a fully blown production application." @default.
- W2583148087 created "2017-02-10" @default.
- W2583148087 creator A5025772657 @default.
- W2583148087 creator A5089334093 @default.
- W2583148087 date "2017-07-01" @default.
- W2583148087 modified "2023-09-30" @default.
- W2583148087 title "Prototyping a GPGPU Neural Network for Deep-Learning Big Data Analysis" @default.
- W2583148087 cites W1981069195 @default.
- W2583148087 cites W1988221687 @default.
- W2583148087 cites W1999183681 @default.
- W2583148087 cites W2051228319 @default.
- W2583148087 cites W2052627427 @default.
- W2583148087 cites W2076063813 @default.
- W2583148087 cites W2097533491 @default.
- W2583148087 cites W2128909582 @default.
- W2583148087 cites W2133218851 @default.
- W2583148087 cites W2342249984 @default.
- W2583148087 cites W4239045242 @default.
- W2583148087 doi "https://doi.org/10.1016/j.bdr.2017.01.005" @default.
- W2583148087 hasPublicationYear "2017" @default.
- W2583148087 type Work @default.
- W2583148087 sameAs 2583148087 @default.
- W2583148087 citedByCount "23" @default.
- W2583148087 countsByYear W25831480872017 @default.
- W2583148087 countsByYear W25831480872018 @default.
- W2583148087 countsByYear W25831480872019 @default.
- W2583148087 countsByYear W25831480872020 @default.
- W2583148087 countsByYear W25831480872021 @default.
- W2583148087 countsByYear W25831480872022 @default.
- W2583148087 countsByYear W25831480872023 @default.
- W2583148087 crossrefType "journal-article" @default.
- W2583148087 hasAuthorship W2583148087A5025772657 @default.
- W2583148087 hasAuthorship W2583148087A5089334093 @default.
- W2583148087 hasConcept C108583219 @default.
- W2583148087 hasConcept C111919701 @default.
- W2583148087 hasConcept C119857082 @default.
- W2583148087 hasConcept C121684516 @default.
- W2583148087 hasConcept C124101348 @default.
- W2583148087 hasConcept C154945302 @default.
- W2583148087 hasConcept C21442007 @default.
- W2583148087 hasConcept C2522767166 @default.
- W2583148087 hasConcept C2984842247 @default.
- W2583148087 hasConcept C41008148 @default.
- W2583148087 hasConcept C50630238 @default.
- W2583148087 hasConcept C50644808 @default.
- W2583148087 hasConcept C519991488 @default.
- W2583148087 hasConcept C75684735 @default.
- W2583148087 hasConceptScore W2583148087C108583219 @default.
- W2583148087 hasConceptScore W2583148087C111919701 @default.
- W2583148087 hasConceptScore W2583148087C119857082 @default.
- W2583148087 hasConceptScore W2583148087C121684516 @default.
- W2583148087 hasConceptScore W2583148087C124101348 @default.
- W2583148087 hasConceptScore W2583148087C154945302 @default.
- W2583148087 hasConceptScore W2583148087C21442007 @default.
- W2583148087 hasConceptScore W2583148087C2522767166 @default.
- W2583148087 hasConceptScore W2583148087C2984842247 @default.
- W2583148087 hasConceptScore W2583148087C41008148 @default.
- W2583148087 hasConceptScore W2583148087C50630238 @default.
- W2583148087 hasConceptScore W2583148087C50644808 @default.
- W2583148087 hasConceptScore W2583148087C519991488 @default.
- W2583148087 hasConceptScore W2583148087C75684735 @default.
- W2583148087 hasFunder F4320334779 @default.
- W2583148087 hasLocation W25831480871 @default.
- W2583148087 hasOpenAccess W2583148087 @default.
- W2583148087 hasPrimaryLocation W25831480871 @default.
- W2583148087 hasRelatedWork W3014300295 @default.
- W2583148087 hasRelatedWork W3164822677 @default.
- W2583148087 hasRelatedWork W4223943233 @default.
- W2583148087 hasRelatedWork W4225161397 @default.
- W2583148087 hasRelatedWork W4312200629 @default.
- W2583148087 hasRelatedWork W4320068940 @default.
- W2583148087 hasRelatedWork W4360585206 @default.
- W2583148087 hasRelatedWork W4364306694 @default.
- W2583148087 hasRelatedWork W4380075502 @default.
- W2583148087 hasRelatedWork W4380086463 @default.
- W2583148087 hasVolume "8" @default.
- W2583148087 isParatext "false" @default.
- W2583148087 isRetracted "false" @default.
- W2583148087 magId "2583148087" @default.
- W2583148087 workType "article" @default.