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1、CHAPTER16HowtoFeedandCareforYourMachine-LearningExpertsPeteWardenMachinelearningisacraftaswellasascience,andforthebestresultsyou’lloftenneedtoturntoexperiencedspecialists.Noteveryteamhasenoughinterestingproblemstojustifyafull-timemachine-learningposition,
2、though.Asthevalueoftheapproachbe‐comesbetter-known,thedemandforpart-timeorproject-basedmachine-learningworkhasgrown,butit’softenhardforatraditionalengineeringteamtoeffectivelyworkwithoutsideexpertsinthefield.I’mgoingtotalkaboutsomeofthethingsIlearnedwhile
3、runninganoutsourcedprojectthroughKaggle,1acommunityofthousandsofresearcherswhoparticipateindatacompetitionsmodeledontheNetflixPrize.Thiswasanextremeexampleofoutsourcing:weliterallyhandedoveradataset,ashortdescrip‐tion,andasuccessmetrictoalargegroupofstran
4、gers.Ithadalmostnoneofthetra‐ditionalinteractionsyou’dexpect,butitdidteachmevaluablelessonsthatapplytoanyinteractionswithmachine-learningspecialists.DefinetheProblemMycompanyJetpaccreatesatravelmagazinewrittenbyyourfriends,usingvacationphotosthey’veshared
5、withyouonFacebookandothersocialservices.Theaverageuserhashadovertwohundredthousandpicturessharedwiththem,sowehavealottochoosefrom.Unfortunately,manyofthemarenotverygood,atleastforourpurposes.Whenweshowedpeopleourprototype,mostwouldbeturnedoffbythepoorqual
6、ityoftheimages,soweknewweneededasolutionthatwouldhelpuspickoutthemostbeautifulfromthedeeppooltowhichwehadaccess.1.“Kaggle:makingdatascienceasport.”(http://www.kaggle.com/)195Thisintentionwastoovaguetobuildon,though.Wehadtodecideexactlywhatwewantedourproce
7、sstoproduce,andhencewhatquestionwewantedtoanswerabouteachphoto.Thisisthecrucialfirststepforanymachine-learningapplication.Unlessyouknowwhatyourgoalis,you’llneverbeabletobuildasuccessfulsolution.Oneofthekeyqualitiesyouneedissomedegreeofobjectivityandrepeat
8、ability.If,likeme,you’retryingtopredictahuman’sreaction,it’snogoodifitvariesunpredictablyfrompersontoperson.Asking“Isthisagoodflavoroficecream?”mightbeagoodmarketresearchquestion,buttheresultsaysmoreaboutanindividua
