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Learn Amazon SageMaker
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Quicklybuildanddeploymachinelearningmodelswithoutmanaginginfrastructure,andimproveproductivityusingAmazonSageMaker’scapabilitiessuchasAmazonSageMakerStudio,Autopilot,Experiments,Debugger,andModelMonitorKeyFeatures*Build,train,anddeploymachinelearningmodelsquicklyusingAmazonSageMaker*Analyze,detect,andreceivealertsrelatingtovariousbusinessproblemsusingmachinelearningalgorithmsandtechniques*Improveproductivitybytrainingandfine-tuningmachinelearningmodelsinproductionBookDescriptionAmazonSageMakerenablesyoutoquicklybuild,train,anddeploymachinelearning(ML)modelsatscale,withoutmanaginganyinfrastructure.IthelpsyoufocusontheMLproblemathandanddeployhigh-qualitymodelsbyremovingtheheavyliftingtypicallyinvolvedineachstepoftheMLprocess.ThisbookisacomprehensiveguidefordatascientistsandMLdeveloperswhowanttolearntheinsandoutsofAmazonSageMaker.You’llunderstandhowtousevariousmodulesofSageMakerasasingletoolsettosolvethechallengesfacedinML.Asyouprogress,you’llcoverfeaturessuchasAutoML,built-inalgorithmsandframeworks,andtheoptionforwritingyourowncodeandalgorithmstobuildMLmodels.Later,thebookwillshowyouhowtointegrateAmazonSageMakerwithpopulardeeplearninglibrariessuchasTensorFlowandPyTorchtoincreasethecapabilitiesofexistingmodels.You’llalsolearntogetthemodelstoproductionfasterwithminimumeffortandatalowercost.Finally,you’llexplorehowtouseAmazonSageMakerDebuggertoanalyze,detect,andhighlightproblemstounderstandthecurrentmodelstateandimprovemodelaccuracy.BytheendofthisAmazonbook,you’llbeabletouseAmazonSageMakeronthefullspectrumofMLworkflows,fromexperimentation,training,andmonitoringtoscaling,deployment,andautomation.Whatyouwilllearn*Createandautomateend-to-endmachinelearningworkflowsonAmazonWebServices(AWS)*Becomewell-versedwithdataannotationandpreparationtechniques*UseAutoMLfeaturestobuildandtrainmachinelearningmodelswithAutoPilot*Createmodelsusingbuilt-inalgorithmsandframeworksandyourowncode*TraincomputervisionandNLPmodelsusingreal-worldexamples*Covertrainingtechniquesforscaling,modeloptimization,modeldebugging,andcostoptimization*AutomatedeploymenttasksinavarietyofconfigurationsusingSDKandseveralautomationtoolsWhothisbookisforThisbookisforsoftwareengineers,machinelearningdevelopers,datascientists,andAWSuserswhoarenewtousingAmazonSageMakerandwanttobuildhigh-qualitymachinelearningmodelswithoutworryingaboutinfrastructure.KnowledgeofAWSbasicsisrequiredtograsptheconceptscoveredinthisbookmoreeffectively.SomeunderstandingofmachinelearningconceptsandthePythonprogramminglanguagewillalsobebeneficial.

Julien Simon;Francesco Pochetti ·統(tǒng)計 ·10.1萬字

時代節(jié)點的眺望
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走過“深改元年”的2014年和“深改關(guān)鍵年”的2015年,我國經(jīng)濟在考驗中穩(wěn)步前行。作為“十三五”開局之年,2016年必將成為我國經(jīng)濟發(fā)展道路上的重要里程碑。“十三五”規(guī)劃建議所提出的“創(chuàng)新、協(xié)調(diào)、綠色、開放、共享”五大發(fā)展理念,高屋建瓴地指明了我國經(jīng)濟未來的發(fā)展方向,其中與國民經(jīng)濟密切相關(guān)的供給側(cè)改革、中國制造2025、“互聯(lián)網(wǎng)+”、“一帶一路”、京津冀一體化和長江經(jīng)濟帶、精準扶貧與脫貧攻堅等重要課題,也成為社會關(guān)注的熱點。2016年“兩會”隆重召開之際,北京大學經(jīng)濟學院組織學院在職教授、專家學者,以筆談的形式探討我國經(jīng)濟改革與發(fā)展大計,為國家經(jīng)濟的可持續(xù)發(fā)展與制度創(chuàng)新提供智力支持。本書匯集了來自不同研究領(lǐng)域的57名學者針對中國當前和未來經(jīng)濟狀況撰寫的70篇分析文章,廣泛涉及我國經(jīng)濟發(fā)展領(lǐng)域的一些重大課題,如全面實現(xiàn)小康社會、產(chǎn)業(yè)結(jié)構(gòu)調(diào)整轉(zhuǎn)型與升級、對外開放政策、財政貨幣制度和宏觀經(jīng)濟走勢、醫(yī)療改革進程、信用制度和征信改革、房地產(chǎn)市場等,具有較高的學術(shù)水平和重要的現(xiàn)實指導意義。

孫祁祥 ·理論 ·18.9萬字

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