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LEADER 00000cgm  2200445Ma 4500 
001    1143019265 
003    OCoLC 
005    20201009145526.8 
006    m     o  d         
007    cr cnu|||||||| 
008    200220s2020    xx ---            vleng   
019    1191058778|a1193323071|a1196278809 
020    |z0636920371007 
024 8  0636920371021 
035    (OCoLC)1143019265|z(OCoLC)1191058778|z(OCoLC)1193323071
040    AU@|beng|cAU@|dUAB|dSTF 
049    MAIN 
100 1  Kejriwal, Mayank,|eauthor. 
245 10 Executive Briefing :|bAn age of embeddings|h[electronic 
       resource] /|cKejriwal, Mayank. 
250    1st edition. 
264  1 |bO'Reilly Media, Inc.,|c2020. 
300    1 online resource (1 video file, approximately 45 min.) 
336    two-dimensional moving image|btdi|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    video file 
520    Word embeddings first emerged as a revolutionary technique
       in natural language processing (NLP) in the last decade, 
       allowing machines to read large reams of unlabeled text 
       and automatically answer analogical questions such as, 
       "What is to man as queen is to woman?" Modern embeddings 
       leverage advances in deep neural networks to be effective.
       Following the success of word embeddings, there have been 
       massive efforts in both academia and industry to embed all
       kinds of data, including images, speech, video, entire 
       sentences, phrases and documents, structured data, and 
       even computer programs. These piecemeal approaches are now
       starting to converge, drawing on a similar mix of 
       techniques. Mayank Kejriwal (USC Information Sciences 
       Institute) explores the ongoing movement that's attempting
       to embed every conceivable kind of data, sometimes jointly,
       to build ever-more powerful predictive models. Mayank 
       makes a business case for why you should care about 
       embeddings and how you can position them as your 
       organization's secret sauce within a broader AI strategy. 
       Prerequisite knowledge Experience implementing or 
       deploying real-world machine learning projects, especially
       using neural networks (useful but not required) What 
       you'll learn Learn what embeddings are and why they're so 
       useful for predictive analytics Discover how embeddings 
       can bolster your organization's AI strategy This session 
       is from the 2019 O'Reilly Artificial Intelligence 
       Conference in San Jose, CA. 
533    Electronic reproduction.|bBoston, MA :|cSafari.|nAvailable
       via World Wide Web. 
538    Mode of access: World Wide Web. 
542    |fCopyright  O'Reilly Media, Inc. 
550    Made available through: Safari, an O'Reilly Media Company.
588    Online resource; Title from title screen (viewed February 
       28, 2020) 
710 2  Safari, an O'Reilly Media Company. 
990    ProQuest Safari|bO'Reilly Online Learning: Academic/Public
       Library Edition|c2020-10-09|yKB collection name change
990    ProQuest Safari|bO'Reilly Safari Learning Platform: 
       Academic edition|c2020-08-21|yAdded to collection 
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