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企業內部審計實務詳解:審計程序+實戰技法+案例解析
會員

內部審計是在組織內部進行的一種獨立客觀的監督和評價活動,通過對企業的管理效能和經營決策進行評審,可以全面有效地發現企業管理環節中的薄弱方面。為了能使企業實現其組織目標,合理審查和評價審查和評價經營活動及內部控制的適當性、合法性和有效性,我們特意編寫了本書?!镀髽I內部審計實務詳解》共包括六篇:內部審計理論基礎、內部審計作業流程、內部審計技術與方法、基礎內部審計實務、按目標分類的內部審計實務指南和內部審計管理;共23章內容。首先從理論層面出發,介紹內部審計的內容、作業和技術;之后詳細介紹內部審計不同項目、不同分類的實務操作,包括合規性審計、保證性審計、績效達標審計、績效提高審計等;最后介紹內部審計的管理,對內部審計的效果進行評價,提高內部審計的有效性?!镀髽I內部審計實務詳解》嚴格依據《中國內部審計準則》和《企業內部控制基本規范》編寫而成,對企業內部審計工作的諸多方面進行了詳細的解讀和指引,濃縮了企業內部審計的全部精華,旨在為企業的內部審計工作提供全面、準確的實務操作指南,提高內部審計工作者的業務操作水平。

企業內部審計編審委員會編著 ·統計 ·46.7萬字

Learn Amazon SageMaker
會員

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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