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dc.contributor.authorLeung, Jackie
dc.date.accessioned2018-10-16T15:40:53Z
dc.date.available2018-10-16T15:40:53Z
dc.date.issued2018-08
dc.identifier.urihttp://hdl.handle.net/1951/70477
dc.description.abstractConvolutional neural networks (CNNs) have gained global recognition in advancing the field of artificial intelligence and have had great successes in a wide array of applications including computer vision, speech and natural language processing. However, due to the rise of big data and increased complexity of tasks, the efficiency of training CNNs have been severely impacted. To achieve state-of-art results, CNNs require tens to hundreds of millions of parameters that need to be fine-tuned, resulting in extensive training time and high computational cost. To overcome these obstacles, this thesis takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. Close examination of the implementation of MapReduce based CNNs as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high accuracy in classification and improvements in speedup, scaleup and sizeup compared to the standard algorithm.en_US
dc.language.isoen_USen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectResearch Subject Categories::SOCIAL SCIENCES::Statistics, computer and systems science::Informatics, computer and systems scienceen_US
dc.subjectConvolutional neural networksen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDistributed frameworksen_US
dc.subjectCloud computingen_US
dc.subjectCNN algorithmen_US
dc.subjectMapReduceen_US
dc.titleMapReduce based convolutional neural networksen_US
dc.typeThesisen_US


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Attribution-NonCommercial-NoDerivs 3.0 United States
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States