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Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible. If you're a developer or data scientist new to NLP and deep learning, this practical guide shows you how to apply these methods using PyTorch, a Python-based deep learning library.
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NaturalLanguageProcessing
withPyTorch
BuildIntelligentLanguageApplicationsUsingDeep
Learning
DelipRaoandBrianMcMahan
NaturalLanguageProcessingwithPyTorch
byDelipRaoandBrianMcMahanCopyright©2019DelipRaoandBrian
McMahan.Allrightsreserved.
PrintedintheUnitedStatesofAmerica.
PublishedbyO’ReillyMedia,Inc.,1005GravensteinHighwayNorth,
Sebastopol,CA95472.
O’Reillybooksmaybepurchasedforeducational,business,orsalespromotional
use.Onlineeditionsarealsoavailableformosttitles(http://oreilly.com/safari).
Formoreinformation,contactourcorporate/institutionalsalesdepartment:800-
998-9938orcorporate@oreilly.com.
AcquisitionEditor:RachelRoumeliotis DevelopmentEditor:JeffBleiel
ProductionEditor:NanBarber Copyeditor:OctalPublishing,LLC
Proofreader:RachelHead Indexer:JudyMcConville
InteriorDesigner:DavidFutato CoverDesigner:KarenMontgomery
Illustrator:RebeccaDemarest
February2019:FirstEdition
RevisionHistoryfortheFirstEdition
2019-01-16:FirstRelease
Seehttp://oreilly.com/catalog/errata.csp?isbn=9781491978238forrelease
details.
TheO’ReillylogoisaregisteredtrademarkofO’ReillyMedia,Inc.Natural
LanguageProcessingwithPyTorch,thecoverimage,andrelatedtradedressare
trademarksofO’ReillyMedia,Inc.
Theviewsexpressedinthisworkarethoseoftheauthors,anddonotrepresent
thepublisher’sviews.Whilethepublisherandtheauthorshaveusedgoodfaith
effortstoensurethattheinformationandinstructionscontainedinthisworkare
accurate,thepublisherandtheauthorsdisclaimallresponsibilityforerrorsor
omissions,includingwithoutlimitationresponsibilityfordamagesresulting
fromtheuseoforrelianceonthiswork.Useoftheinformationandinstructions
containedinthisworkisatyourownrisk.Ifanycodesamplesorother
technologythisworkcontainsordescribesissubjecttoopensourcelicensesor
theintellectualpropertyrightsofothers,itisyourresponsibilitytoensurethat
yourusethereofcomplieswithsuchlicensesand/orrights.
978-1-491-97823-8
[LSI]
Preface
Thisbookaimstobringnewcomerstonaturallanguageprocessing(NLP)and
deeplearningtoatastingtablecoveringimportanttopicsinbothareas.Bothof
thesesubjectareasaregrowingexponentially.Asitintroducesbothdeep
learningandNLPwithanemphasisonimplementation,thisbookoccupiesan
importantmiddleground.Whilewritingthebook,wehadtomakedifficult,and
sometimesuncomfortable,choicesonwhatmaterialtoleaveout.Forabeginner
reader,wehopethebookwillprovideastrongfoundationinthebasicsanda
glimpseofwhatispossible.Machinelearning,anddeeplearninginparticular,is
anexperientialdiscipline,asopposedtoanintellectualscience.Thegenerous
end-to-endcodeexamplesineachchapterinviteyoutopartakeinthat
experience.
Whenwebeganworkingonthebook,westartedwithPyTorch0.2.The
exampleswererevisedwitheachPyTorchupdatefrom0.2to0.4.PyTorch1.0is
duetoreleasearoundwhenthisbookcomesout.Thecodeexamplesinthebook
arePyTorch0.4–compliantandshouldworkastheyarewiththeupcoming
PyTorch1.0release.
Anoteregardingthestyleofthebook.Wehaveintentionallyavoided
mathematicsinmostplaces,notbecausedeeplearningmathisparticularly
difficult(itisnot),butbecauseitisadistractioninmanysituationsfromthe
maingoalofthisbook—toempowerthebeginnerlearner.Likewise,inmany
cases,bothincodeandtext,wehavefavoredexpositionoversuccinctness.
Advancedreadersandexperiencedprogrammerswilllikelyseewaystotighten
upthecodeandsoon,butourchoicewastobeasexplicitaspossiblesoasto
reachthebroadestoftheaudiencethatwewanttoreach.
ConventionsUsedinThisBook
Thefollowingtypographicalconventionsareusedinthisbook:
Italic
1
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