Show simple item record

dc.contributor.authorLemley, Joseph
dc.contributor.authorBazrafkan, Shabab
dc.contributor.authorCorcoran, Peter
dc.date.accessioned2017-08-11T08:42:53Z
dc.date.available2017-08-11T08:42:53Z
dc.date.issued2017-04
dc.identifier.citationLemley, J., Bazrafkan, S., & Corcoran, P. (2017). Deep Learning for Consumer Devices and Services: Pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consumer Electronics Magazine, 6(2), 48-56. doi: 10.1109/MCE.2016.2640698en_IE
dc.identifier.issn2162-2248
dc.identifier.urihttp://hdl.handle.net/10379/6699
dc.description.abstractIn the last few years, we have witnessed an exponential growth in research activity into the advanced training of convolutional neural networks (CNNs), a field that has become known as deep learning. This has been triggered by a combination of the availability of significantly larger data sets, thanks in part to a corresponding growth in big data, and the arrival of new graphics-processing-unit (GPU)-based hardware that enables these large data sets to be processed in reasonable timescales. Suddenly, a wide variety of long-standing problems in machine learning, artificial intelligence, and computer vision have seen significant improvements, often sufficient to break through long-standing performance barriers. Across multiple fields, these achievements have inspired the development of improved tools and methodologies leading to even broader applicability of deep learning. The new generation of smart assistants, such as Alexa, Hello Google, and others, have their roots and learning algorithms tied to deep learning. In this article, we review the current state of deep learning, explain what it is, why it has managed to improve on the long-standing techniques of conventional neural networks, and, most importantly, how you can get started with adopting deep learning into your own research activities to solve both new and old problems and build better, smarter consumer devices and services.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_IE
dc.relation.ispartofIEEE Consumer Electronics Magazineen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectDeep learningen_IE
dc.subjectConsumer devicesen_IE
dc.subjectConsumer servicesen_IE
dc.subjectMachine learningen_IE
dc.subjectArtificial intelligenceen_IE
dc.subjectComputer visionen_IE
dc.subjectConvolutional neural networksen_IE
dc.subjectConvolutional neural networksen_IE
dc.subjectCNNen_IE
dc.subjectBig dataen_IE
dc.subjectGraphics processing unit based hardwareen_IE
dc.subjectGPU based hardwareen_IE
dc.subjectSmart assistantsen_IE
dc.subjectAlexaen_IE
dc.subjectHello Googleen_IE
dc.titleDeep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer visionen_IE
dc.typeArticleen_IE
dc.date.updated2017-08-03T15:57:25Z
dc.identifier.doi10.1109/MCE.2016.2640698
dc.local.publishedsourcehttp://dx.doi.org/10.1109/MCE.2016.2640698en_IE
dc.description.peer-reviewedpeer-reviewed
dc.contributor.funder|~|
dc.internal.rssid12926713
dc.local.contactPeter Corcoran, Electrical & Electronic Eng, Room 3041, Engineering Building, Nui Galway. 2764 Email: peter.corcoran@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionSUBMITTED
nui.item.downloads2262


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

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