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Hands/On Machine Learning with C++
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ImplementsupervisedandunsupervisedmachinelearningalgorithmsusingC++librariessuchasPyTorchC++API,Caffe2,Shogun,Shark-ML,mlpack,anddlibwiththehelpofreal-worldexamplesanddatasetsKeyFeatures.Becomefamiliarwithdataprocessing,performancemeasuring,andmodelselectionusingvariousC++libraries.Implementpracticalmachinelearninganddeeplearningtechniquestobuildsmartmodels.DeploymachinelearningmodelstoworkonmobileandembeddeddevicesBookDescriptionC++canmakeyourmachinelearningmodelsrunfasterandmoreefficiently.Thishandyguidewillhelpyoulearnthefundamentalsofmachinelearning(ML),showingyouhowtouseC++librariestogetthemostoutofyourdata.ThisbookmakesmachinelearningwithC++forbeginnerseasywithitsexample-basedapproach,demonstratinghowtoimplementsupervisedandunsupervisedMLalgorithmsthroughreal-worldexamples.Thisbookwillgetyouhands-onwithtuningandoptimizingamodelfordifferentusecases,assistingyouwithmodelselectionandthemeasurementofperformance.You’llcovertechniquessuchasproductrecommendations,ensemblelearning,andanomalydetectionusingmodernC++librariessuchasPyTorchC++API,Caffe2,Shogun,Shark-ML,mlpack,anddlib.Next,you’llexploreneuralnetworksanddeeplearningusingexamplessuchasimageclassificationandsentimentanalysis,whichwillhelpyousolvevariousproblems.Later,you’lllearnhowtohandleproductionanddeploymentchallengesonmobileandcloudplatforms,beforediscoveringhowtoexportandimportmodelsusingtheONNXformat.BytheendofthisC++book,youwillhavereal-worldmachinelearningandC++knowledge,aswellastheskillstouseC++tobuildpowerfulMLsystems.Whatyouwilllearn.ExplorehowtoloadandpreprocessvariousdatatypestosuitableC++datastructures.EmploykeymachinelearningalgorithmswithvariousC++libraries.Understandthegrid-searchapproachtofindthebestparametersforamachinelearningmodel.ImplementanalgorithmforfilteringanomaliesinuserdatausingGaussiandistribution.Improvecollaborativefilteringtodealwithdynamicuserpreferences.UseC++librariesandAPIstomanagemodelstructuresandparameters.ImplementaC++programtosolveimageclassificationtaskswithLeNetarchitectureWhothisbookisforYouwillfindthisC++machinelearningbookusefulifyouwanttogetstartedwithmachinelearningalgorithmsandtechniquesusingthepopularC++language.AswellasbeingausefulfirstcourseinmachinelearningwithC++,thisbookwillalsoappealtodataanalysts,datascientists,andmachinelearningdeveloperswhoarelookingtoimplementdifferentmachinelearningmodelsinproductionusingvarieddatasetsandexamples.WorkingknowledgeoftheC++programminglanguageismandatorytogetstartedwiththisbook.

Kirill Kolodiazhnyi ·電子通信 ·10.7萬字

基于加權多維標度的無線信號定位理論與方法
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本書系統闡述了基于加權多維標度的無線信號定位理論與方法,全書包含3大部分14章內容。第1部分為基礎知識篇(第1~3章),包括緒論、數學預備知識及無線信號定位統計性能分析。第2部分為基本定位方法篇(第4~8章),包括基于TOA觀測信息的加權多維標度定位方法、基于TDOA觀測信息的加權多維標度定位方法、基于RSS觀測信息的加權多維標度定位方法、基于TOA/FOA觀測信息的加權多維標度定位方法及基于TDOA/FDOA觀測信息的加權多維標度定位方法。第3部分為拓展定位方法篇(第9~14章),包括傳感器位置誤差存在條件下基于TOA觀測信息的加權多維標度定位方法、傳感器位置誤差存在條件下基于TDOA觀測信息的加權多維標度定位方法、基于TOA/FOA觀測信息的多不相關源加權多維標度定位方法、校正源存在條件下基于TDOA觀測信息的加權多維標度定位方法、面向無線傳感網節點定位的加權多維標度TOA定位方法及面向無線傳感網節點定位的加權多維標度RSS定位方法。本書可作為高等院校信號與信息處理、通信與信息系統、控制科學與工程、應用數學等專業的專題閱讀材料或研究生選修教材,也可作為通信、雷達、電子、導航測繪、航天航空等領域的科學工作者和工程技術人員自學或研究的參考書。

王鼎 ·電子通信 ·10.5萬字

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