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A+H股雙重審計管制取消的經(jīng)濟(jì)后果:基于權(quán)益成本和審計質(zhì)量的研究
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作為一項在促進(jìn)會計師事務(wù)所增強獨立性和積累經(jīng)驗、提高上市公司治理水平和會計信息質(zhì)量等方面均具有重要作用的審計制度,雙重審計制度及其變遷一直是會計與審計理論界、實務(wù)界十分關(guān)注的話題。然而,現(xiàn)有文獻(xiàn)基本局限于研究雙重審計管制取消導(dǎo)致的審計方面的后果,未充分關(guān)注該政策變化對資本市場的潛在影響;此外,現(xiàn)存關(guān)于雙重審計管制取消的審計后果的研究缺乏對引起公司放棄雙重審計后審計質(zhì)量下降的內(nèi)在機制的深入探索。對此,本書基于審計需求、審計管制、審計供給以及遵循效應(yīng)相關(guān)理論,采用我國上市公司數(shù)據(jù),借助經(jīng)典模型測算權(quán)益成本,新建指標(biāo)反映驅(qū)動審計質(zhì)量變化的內(nèi)在機制,運用多元回歸分析、傾向得分匹配、變化分析、混雜變量影響閾值分析等多種方法,對雙重審計管制取消在資本市場和審計市場上產(chǎn)生的經(jīng)濟(jì)后果進(jìn)行理論闡釋與實證檢驗。本書為理解雙重審計制度及其變遷的影響提供新的參考,對我國今后的審計制度安排與行業(yè)發(fā)展具有重要啟示。

張睿 ·統(tǒng)計 ·12萬字

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(tǒng)計 ·10.1萬字

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