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智能制造探索與實踐(二):試點示范項目匯編(電子信息行業卷)
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為深入貫徹落實制造強國戰略的總體安排,加快建設制造強國,自2015年,已連續多年組織實施智能制造試點示范專項行動。三年來,遴選確定了206個試點示范項目,覆蓋30個?。ㄊ小^)、82個行業,有效帶動社會投資770億元。這些試點示范項目智能化改造前后,在企業提質增效、降本減耗、提高核心競爭力等方面發揮了積極作用,有力支撐并帶動了制造業轉型升級。試點示范企業通過先行先試探索形成了一批較成熟、可復制、可推廣的智能制造新模式。在各方面的共同努力下,專項行動取得了明顯成效。智能制造推進體系基本形成,核心裝備供給能力持續增強,集成服務能力不斷提高,基礎支撐能力不斷夯實,新模式推廣應用成效明顯。在組織出版了首批46個智能制造試點示范項目案例匯編之后,為相關地區、行業、企業實施智能制造提供了借鑒與參考,形成了很好的效果。為擴大試點示范效應,加快示范企業典型經驗的推廣應用,本書是對2016年和2017年的智能制造試點示范項目實施情況進行了梳理匯編成書,分為《電子信息行業卷》《裝備制造行業卷》《原材料行業卷》《消費品行業卷》四個分冊,以持續營造全社會推廣智能制造的良好氛圍。

《智能制造探索與實踐》編寫組編寫 ·部門經濟 ·10.9萬字

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 ·統計 ·10.1萬字

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