Loading…
Applied Machine Learning Strategies
Recent advances in machine learning (ML), fueled by new frameworks and algorithms, more powerful computing architectures, scalable cloud-based services, and availability of large-scale data sets, have enabled scientists and engineers to tackle more complex problems than ever before. Computer hardwar...
Saved in:
| Published in: | IEEE potentials 2020-05, Vol.39 (3), p.38-42 |
|---|---|
| Main Authors: | , , , , |
| Format: | Article |
| Language: | English |
| Subjects: | |
| Citations: | Items that this one cites |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| cited_by | |
|---|---|
| cites | cdi_FETCH-LOGICAL-c160t-799d5263b13f2fe4c4152b4063e1d9d6674b35e8dabe15bcaa22d034414719773 |
| container_end_page | 42 |
| container_issue | 3 |
| container_start_page | 38 |
| container_title | IEEE potentials |
| container_volume | 39 |
| creator | Israel, Steven A. Sallee, Philip Tanner, Franklin Goldstein, Jonathan Zabel, Shane |
| description | Recent advances in machine learning (ML), fueled by new frameworks and algorithms, more powerful computing architectures, scalable cloud-based services, and availability of large-scale data sets, have enabled scientists and engineers to tackle more complex problems than ever before. Computer hardware has made tremendous leaps in processing power, bit depth, caching, and storage. Graphics processing units, developed originally for the gaming industry, provide a parallel processing capability that is ideally suited to computer vision (CV) and the simulation of artificial neural networks. The expansion to cloud-based services allows researchers virtually unlimited scaling of resources to tackle problems having millions of input attributes, with output domains up to thousands of potentially nonexclusive classes. |
| doi_str_mv | 10.1109/MPOT.2019.2927899 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1109_MPOT_2019_2927899</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9086834</ieee_id><sourcerecordid>2400103398</sourcerecordid><originalsourceid>FETCH-LOGICAL-c160t-799d5263b13f2fe4c4152b4063e1d9d6674b35e8dabe15bcaa22d034414719773</originalsourceid><addsrcrecordid>eNo9kE1Lw0AQhhdRMFZ_gHgp9Jy4s997LMUvSKlgPS-bZFJTahp304P_3oQUTwPD874zPITcA80AqH1cv2-2GaNgM2aZNtZekASkNClozS5JQodlqpQw1-Qmxj2lICSXCVksu-7QYDVf-_KraXGeow9t0-7mH33wPe4ajLfkqvaHiHfnOSOfz0_b1Wuab17eVss8LUHRPtXWVpIpXgCvWY2iFCBZIajiCJWtlNKi4BJN5QsEWZTeM1ZRLgQIDVZrPiOLqbcLx58Txt7tj6fQDicdE8PHlHNrBgomqgzHGAPWrgvNtw-_DqgbXbjRhRtduLOLIfMwZRpE_OctNcpwwf8ANDFYMA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2400103398</pqid></control><display><type>article</type><title>Applied Machine Learning Strategies</title><source>IEEE Xplore (Online service)</source><creator>Israel, Steven A. ; Sallee, Philip ; Tanner, Franklin ; Goldstein, Jonathan ; Zabel, Shane</creator><creatorcontrib>Israel, Steven A. ; Sallee, Philip ; Tanner, Franklin ; Goldstein, Jonathan ; Zabel, Shane</creatorcontrib><description>Recent advances in machine learning (ML), fueled by new frameworks and algorithms, more powerful computing architectures, scalable cloud-based services, and availability of large-scale data sets, have enabled scientists and engineers to tackle more complex problems than ever before. Computer hardware has made tremendous leaps in processing power, bit depth, caching, and storage. Graphics processing units, developed originally for the gaming industry, provide a parallel processing capability that is ideally suited to computer vision (CV) and the simulation of artificial neural networks. The expansion to cloud-based services allows researchers virtually unlimited scaling of resources to tackle problems having millions of input attributes, with output domains up to thousands of potentially nonexclusive classes.</description><identifier>ISSN: 0278-6648</identifier><identifier>EISSN: 1558-1772</identifier><identifier>DOI: 10.1109/MPOT.2019.2927899</identifier><identifier>CODEN: IEPTDF</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Artificial neural networks ; Automobiles ; Caching ; Cloud computing ; Computer simulation ; Computer vision ; Gallium nitride ; Generators ; Graphics processing units ; Machine learning ; Neural networks ; Parallel processing ; Training ; Training data</subject><ispartof>IEEE potentials, 2020-05, Vol.39 (3), p.38-42</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c160t-799d5263b13f2fe4c4152b4063e1d9d6674b35e8dabe15bcaa22d034414719773</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9086834$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,54796</link.rule.ids></links><search><creatorcontrib>Israel, Steven A.</creatorcontrib><creatorcontrib>Sallee, Philip</creatorcontrib><creatorcontrib>Tanner, Franklin</creatorcontrib><creatorcontrib>Goldstein, Jonathan</creatorcontrib><creatorcontrib>Zabel, Shane</creatorcontrib><title>Applied Machine Learning Strategies</title><title>IEEE potentials</title><addtitle>MPOT</addtitle><description>Recent advances in machine learning (ML), fueled by new frameworks and algorithms, more powerful computing architectures, scalable cloud-based services, and availability of large-scale data sets, have enabled scientists and engineers to tackle more complex problems than ever before. Computer hardware has made tremendous leaps in processing power, bit depth, caching, and storage. Graphics processing units, developed originally for the gaming industry, provide a parallel processing capability that is ideally suited to computer vision (CV) and the simulation of artificial neural networks. The expansion to cloud-based services allows researchers virtually unlimited scaling of resources to tackle problems having millions of input attributes, with output domains up to thousands of potentially nonexclusive classes.</description><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Automobiles</subject><subject>Caching</subject><subject>Cloud computing</subject><subject>Computer simulation</subject><subject>Computer vision</subject><subject>Gallium nitride</subject><subject>Generators</subject><subject>Graphics processing units</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Parallel processing</subject><subject>Training</subject><subject>Training data</subject><issn>0278-6648</issn><issn>1558-1772</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNo9kE1Lw0AQhhdRMFZ_gHgp9Jy4s997LMUvSKlgPS-bZFJTahp304P_3oQUTwPD874zPITcA80AqH1cv2-2GaNgM2aZNtZekASkNClozS5JQodlqpQw1-Qmxj2lICSXCVksu-7QYDVf-_KraXGeow9t0-7mH33wPe4ajLfkqvaHiHfnOSOfz0_b1Wuab17eVss8LUHRPtXWVpIpXgCvWY2iFCBZIajiCJWtlNKi4BJN5QsEWZTeM1ZRLgQIDVZrPiOLqbcLx58Txt7tj6fQDicdE8PHlHNrBgomqgzHGAPWrgvNtw-_DqgbXbjRhRtduLOLIfMwZRpE_OctNcpwwf8ANDFYMA</recordid><startdate>20200501</startdate><enddate>20200501</enddate><creator>Israel, Steven A.</creator><creator>Sallee, Philip</creator><creator>Tanner, Franklin</creator><creator>Goldstein, Jonathan</creator><creator>Zabel, Shane</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>F28</scope><scope>FR3</scope><scope>L7M</scope></search><sort><creationdate>20200501</creationdate><title>Applied Machine Learning Strategies</title><author>Israel, Steven A. ; Sallee, Philip ; Tanner, Franklin ; Goldstein, Jonathan ; Zabel, Shane</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c160t-799d5263b13f2fe4c4152b4063e1d9d6674b35e8dabe15bcaa22d034414719773</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Automobiles</topic><topic>Caching</topic><topic>Cloud computing</topic><topic>Computer simulation</topic><topic>Computer vision</topic><topic>Gallium nitride</topic><topic>Generators</topic><topic>Graphics processing units</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Parallel processing</topic><topic>Training</topic><topic>Training data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Israel, Steven A.</creatorcontrib><creatorcontrib>Sallee, Philip</creatorcontrib><creatorcontrib>Tanner, Franklin</creatorcontrib><creatorcontrib>Goldstein, Jonathan</creatorcontrib><creatorcontrib>Zabel, Shane</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE potentials</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Israel, Steven A.</au><au>Sallee, Philip</au><au>Tanner, Franklin</au><au>Goldstein, Jonathan</au><au>Zabel, Shane</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applied Machine Learning Strategies</atitle><jtitle>IEEE potentials</jtitle><stitle>MPOT</stitle><date>2020-05-01</date><risdate>2020</risdate><volume>39</volume><issue>3</issue><spage>38</spage><epage>42</epage><pages>38-42</pages><issn>0278-6648</issn><eissn>1558-1772</eissn><coden>IEPTDF</coden><abstract>Recent advances in machine learning (ML), fueled by new frameworks and algorithms, more powerful computing architectures, scalable cloud-based services, and availability of large-scale data sets, have enabled scientists and engineers to tackle more complex problems than ever before. Computer hardware has made tremendous leaps in processing power, bit depth, caching, and storage. Graphics processing units, developed originally for the gaming industry, provide a parallel processing capability that is ideally suited to computer vision (CV) and the simulation of artificial neural networks. The expansion to cloud-based services allows researchers virtually unlimited scaling of resources to tackle problems having millions of input attributes, with output domains up to thousands of potentially nonexclusive classes.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/MPOT.2019.2927899</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0278-6648 |
| ispartof | IEEE potentials, 2020-05, Vol.39 (3), p.38-42 |
| issn | 0278-6648 1558-1772 |
| language | eng |
| recordid | cdi_crossref_primary_10_1109_MPOT_2019_2927899 |
| source | IEEE Xplore (Online service) |
| subjects | Algorithms Artificial neural networks Automobiles Caching Cloud computing Computer simulation Computer vision Gallium nitride Generators Graphics processing units Machine learning Neural networks Parallel processing Training Training data |
| title | Applied Machine Learning Strategies |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T08%3A04%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Applied%20Machine%20Learning%20Strategies&rft.jtitle=IEEE%20potentials&rft.au=Israel,%20Steven%20A.&rft.date=2020-05-01&rft.volume=39&rft.issue=3&rft.spage=38&rft.epage=42&rft.pages=38-42&rft.issn=0278-6648&rft.eissn=1558-1772&rft.coden=IEPTDF&rft_id=info:doi/10.1109/MPOT.2019.2927899&rft_dat=%3Cproquest_cross%3E2400103398%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c160t-799d5263b13f2fe4c4152b4063e1d9d6674b35e8dabe15bcaa22d034414719773%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2400103398&rft_id=info:pmid/&rft_ieee_id=9086834&rfr_iscdi=true |