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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

5G網(wǎng)絡(luò)建設(shè)實(shí)踐與模式創(chuàng)新
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5G網(wǎng)絡(luò)建設(shè)實(shí)踐與模式創(chuàng)新共9章。首先,梳理了5G移動(dòng)通信發(fā)展的歷史與現(xiàn)狀,詳細(xì)闡述了5G網(wǎng)絡(luò)的關(guān)鍵理論基礎(chǔ)和組網(wǎng)架構(gòu);然后根據(jù)5G技術(shù)的發(fā)展路線,分別論述了NSA和SA網(wǎng)絡(luò)的特點(diǎn)和演進(jìn)思路;其次,分析總結(jié)了影響5G網(wǎng)絡(luò)建設(shè)的關(guān)鍵技術(shù)點(diǎn),深入分析了關(guān)鍵技術(shù)在實(shí)際落地應(yīng)用過程中,所需要的措施、優(yōu)缺點(diǎn)、常見問題以及5G建設(shè)和管理的應(yīng)對(duì)方法;最后,梳理了5G基站工程的建設(shè)開通驗(yàn)收全流程,并深度結(jié)合深圳電信的工程建設(shè)經(jīng)驗(yàn),詳細(xì)介紹了5G基站建設(shè)過程中的具體步驟。本書在中國電信和中國聯(lián)通共建共享5G網(wǎng)絡(luò)的基礎(chǔ)上,吸納了深圳電信的5G基站工程經(jīng)驗(yàn),為全國5G網(wǎng)絡(luò)建設(shè)提供指引。本書可以作為學(xué)習(xí)5G基站建設(shè)要點(diǎn)的參考書,也可以供從事5G基站技術(shù)、建設(shè)領(lǐng)域工作的工程技術(shù)人員閱讀。

李江 羅宏 馮煒 方少敏 曾沂粲 張慶利編著 ·電子通信 ·9.4萬字

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