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Applied Deep Learning and Computer Vision for Self/Driving Cars
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Exploreself-drivingcartechnologyusingdeeplearningandartificialintelligencetechniquesandlibrariessuchasTensorFlow,Keras,andOpenCVKeyFeatures*Buildandtrainpowerfulneuralnetworkmodelstobuildanautonomouscar*Implementcomputervision,deeplearning,andAItechniquestocreateautomotivealgorithms*OvercomethechallengesfacedwhileautomatingdifferentaspectsofdrivingusingmodernPythonlibrariesandarchitecturesBookDescriptionThankstoanumberofrecentbreakthroughs,self-drivingcartechnologyisnowanemergingsubjectinthefieldofartificialintelligenceandhasshifteddatascientists'focustobuildingautonomouscarsthatwilltransformtheautomotiveindustry.Thisbookisacomprehensiveguidetousedeeplearningandcomputervisiontechniquestodevelopautonomouscars.Startingwiththebasicsofself-drivingcars(SDCs),thisbookwilltakeyouthroughthedeepneuralnetworktechniquesrequiredtogetupandrunningwithbuildingyourautonomousvehicle.Onceyouarecomfortablewiththebasics,you'lldelveintoadvancedcomputervisiontechniquesandlearnhowtousedeeplearningmethodstoperformavarietyofcomputervisiontaskssuchasfindinglanelines,improvingimageclassification,andsoon.Youwillexplorethebasicstructureandworkingofasemanticsegmentationmodelandgettogripswithdetectingcarsusingsemanticsegmentation.Thebookalsocoversadvancedapplicationssuchasbehavior-cloningandvehicledetectionusingOpenCV,transferlearning,anddeeplearningmethodologiestotrainSDCstomimichumandriving.Bytheendofthisbook,you'llhavelearnedhowtoimplementavarietyofneuralnetworkstodevelopyourownautonomousvehicleusingmodernPythonlibraries.Whatyouwilllearn*ImplementdeepneuralnetworkfromscratchusingtheKeraslibrary*Understandtheimportanceofdeeplearninginself-drivingcars*GettogripswithfeatureextractiontechniquesinimageprocessingusingtheOpenCVlibrary*Designasoftwarepipelinethatdetectslanelinesinvideos*Implementaconvolutionalneuralnetwork(CNN)imageclassifierfortrafficsignalsigns*Trainandtestneuralnetworksforbehavioral-cloningbydrivingacarinavirtualsimulator*Discovervariousstate-of-the-artsemanticsegmentationandobjectdetectionarchitecturesWhothisbookisforIfyouareadeeplearningengineer,AIresearcher,oranyonelookingtoimplementdeeplearningandcomputervisiontechniquestobuildself-drivingblueprintsolutions,thisbookisforyou.Anyonewhowantstolearnhowvariousautomotive-relatedalgorithmsarebuilt,willalsofindthisbookuseful.Pythonprogrammingexperience,alongwithabasicunderstandingofdeeplearning,isnecessarytogetthemostofthisbook.

Sumit Ranjan;Dr. S. Senthamilarasu ·專用軟件 ·5.4萬字

Hands/On Simulation Modeling with Python
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EnhanceyoursimulationmodelingskillsbycreatingandanalyzingdigitalprototypesofaphysicalmodelusingPythonprogrammingwiththiscomprehensiveguideKeyFeatures.Learntocreateadigitalprototypeofarealmodelusinghands-onexamples.Evaluatetheperformanceandoutputofyourprototypeusingsimulationmodelingtechniques.UnderstandvariousstatisticalandphysicalsimulationstoimprovesystemsusingPythonBookDescriptionSimulationmodelinghelpsyoutocreatedigitalprototypesofphysicalmodelstoanalyzehowtheyworkandpredicttheirperformanceintherealworld.Withthiscomprehensiveguide,you'llunderstandvariouscomputationalstatisticalsimulationsusingPython.Startingwiththefundamentalsofsimulationmodeling,you'llunderstandconceptssuchasrandomnessandexploredatageneratingprocesses,resamplingmethods,andbootstrappingtechniques.You'llthencoverkeyalgorithmssuchasMonteCarlosimulationsandMarkovdecisionprocesses,whichareusedtodevelopnumericalsimulationmodels,anddiscoverhowtheycanbeusedtosolvereal-worldproblems.Asyouadvance,you'lldevelopsimulationmodelstohelpyougetaccurateresultsandenhancedecision-makingprocesses.Usingoptimizationtechniques,you'lllearntomodifytheperformanceofamodeltoimproveresultsandmakeoptimaluseofresources.Thebookwillguideyouincreatingadigitalprototypeusingpracticalusecasesforfinancialengineering,prototypingprojectmanagementtoimproveplanning,andsimulatingphysicalphenomenausingneuralnetworks.Bytheendofthisbook,you'llhavelearnedhowtoconstructanddeploysimulationmodelsofyourowntoovercomereal-worldchallenges.Whatyouwilllearn.Gainanoverviewofthedifferenttypesofsimulationmodels.Gettogripswiththeconceptsofrandomnessanddatagenerationprocess.Understandhowtoworkwithdiscreteandcontinuousdistributions.WorkwithMonteCarlosimulationstocalculateadefiniteintegral.FindouthowtosimulaterandomwalksusingMarkovchains.Obtainrobustestimatesofconfidenceintervalsandstandarderrorsofpopulationparameters.Discoverhowtouseoptimizationmethodsinreal-lifeapplications.Runefficientsimulationstoanalyzereal-worldsystemsWhothisbookisforHands-OnSimulationModelingwithPythonisforsimulationdevelopersandengineers,modeldesigners,andanyonealreadyfamiliarwiththebasiccomputationalmethodsthatareusedtostudythebehaviorofsystems.ThisbookwillhelpyouexploreadvancedsimulationtechniquessuchasMonteCarlomethods,statisticalsimulations,andmuchmoreusingPython.WorkingknowledgeofPythonprogramminglanguageisrequired.

Giuseppe Ciaburro ·專用軟件 ·8.2萬字

Premiere Pro CC 2015中文版基礎與實例教程(第4版)
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《PremiereProCC2015中文版基礎與實例教程第4版》屬于實例類圖書。全書分為3個部分,共7章。第1部分為基礎入門,主要介紹了影視剪輯基礎理論和Premiere的基礎知識;第2部分為基礎實例演練,通過大量典型和具有代表性的實例講解了關鍵幀動畫和時間線嵌套、視頻切換、視頻特效、字幕的具體應用;第3部分為綜合實例演練,講解了兩個實例的具體制作方法,涉及前面各章知識,并且對部分實例采用了多種方法,旨在拓寬讀者的思路,做到舉一反三。本書內容全面、由淺入深。初學者可從基礎入門部分開始學習,有一定基礎的讀者,可從基礎實例演練部分開始學習。讀者通過本書可以全面、系統地掌握PremiereProCC2015的使用技巧。同時為了幫助大家學習,《PremiereProCC2015中文版基礎與實例教程第4版》通過網盤(獲取方式請見封底)提供全部實例的素材和效果文件,以及相關的高清晰度的多媒體影像教學文件和基礎入門部分的電子課件。《PremiereProCC2015中文版基礎與實例教程第4版》內容豐富、實例典型、講解詳盡,可作為大專院校相關專業師生和社會培訓班學員的教材,也可作為視頻編輯愛好者的自學和參考用書。

張凡等編著 ·專用軟件 ·10.7萬字

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