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Python程序設計開發寶典
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全書共13章,面向Python3.5.x、Python3.6.x和Python3.7.x,重點關注Python內置對象和標準庫對象的高級應用以及比較前沿的一些新技術,偶爾涉及一點擴展庫用法。第1章介紹Python語言編程規范與代碼優化建議、開發環境配置、程序偽編譯與打包。第2章詳解運算符與內置函數的用法。第3章詳解各種序列對象、推導式、切片和序列解包。第4章詳解選擇結構和循環結構,關鍵字else、break與continue。第5章詳解函數的基本用法,可調用對象與修飾器、函數參數、變量的作用域、生成器函數、lambda表達式、函數柯里化、泛型函數、協程函數和回調函數。第6章詳解類的定義、不同類型的成員、依賴注入技術和運算符重載。第7章詳解字符串編碼與格式化方法、字符串對象方法、文本排版與壓縮、漢字拼音有關的技術。第8章詳解正則表達式語法、re模塊、正則表達式對象與match對象。第9章詳解文件對象用法、文件內容操作。第10章詳解文件與文件夾操作。第11章詳解異常處理結構、文檔測試與單元測試、覆蓋測試與軟件性能測試、代碼調試技術。第12章詳解不同類型的并行處理技術。第13章詳解asyncio提供的網絡通信功能。本書不但可以作為Python程序設計教材,還可作為Python開發工程師的指導用書。

董付國 ·程序設計 ·13.4萬字

Python Machine Learning / Second Edition
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UnlockmodernmachinelearninganddeeplearningtechniqueswithPythonbyusingthelatestcutting-edgeopensourcePythonlibraries.AboutThisBook?SecondeditionofthebestsellingbookonMachineLearning?Apracticalapproachtokeyframeworksindatascience,machinelearning,anddeeplearning?UsethemostpowerfulPythonlibrariestoimplementmachinelearninganddeeplearning?GettoknowthebestpracticestoimproveandoptimizeyourmachinelearningsystemsandalgorithmsWhoThisBookIsForIfyouknowsomePythonandyouwanttousemachinelearninganddeeplearning,pickupthisbook.Whetheryouwanttostartfromscratchorextendyourmachinelearningknowledge,thisisanessentialandunmissableresource.Writtenfordevelopersanddatascientistswhowanttocreatepracticalmachinelearninganddeeplearningcode,thisbookisidealfordevelopersanddatascientistswhowanttoteachcomputershowtolearnfromdata.WhatYouWillLearn?Understandthekeyframeworksindatascience,machinelearning,anddeeplearning?HarnessthepowerofthelatestPythonopensourcelibrariesinmachinelearning?Exploremachinelearningtechniquesusingchallengingreal-worlddata?MasterdeepneuralnetworkimplementationusingtheTensorFlowlibrary?Learnthemechanicsofclassificationalgorithmstoimplementthebesttoolforthejob?Predictcontinuoustargetoutcomesusingregressionanalysis?Uncoverhiddenpatternsandstructuresindatawithclustering?DelvedeeperintotextualandsocialmediadatausingsentimentanalysisInDetailMachinelearningiseatingthesoftwareworld,andnowdeeplearningisextendingmachinelearning.Understandandworkatthecuttingedgeofmachinelearning,neuralnetworks,anddeeplearningwiththissecondeditionofSebastianRaschka’sbestsellingbook,PythonMachineLearning.ThoroughlyupdatedusingthelatestPythonopensourcelibraries,thisbookoffersthepracticalknowledgeandtechniquesyouneedtocreateandcontributetomachinelearning,deeplearning,andmoderndataanalysis.Fullyextendedandmodernized,PythonMachineLearningSecondEditionnowincludesthepopularTensorFlowdeeplearninglibrary.Thescikit-learncodehasalsobeenfullyupdatedtoincluderecentimprovementsandadditionstothisversatilemachinelearninglibrary.SebastianRaschkaandVahidMirjalili’suniqueinsightandexpertiseintroduceyoutomachinelearninganddeeplearningalgorithmsfromscratch,andshowyouhowtoapplythemtopracticalindustrychallengesusingrealisticandinterestingexamples.Bytheendofthebook,you’llbereadytomeetthenewdataanalysisopportunitiesintoday’sworld.Ifyou’vereadthefirsteditionofthisbook,you’llbedelightedtofindanewbalanceofclassicalideasandmoderninsightsintomachinelearning.Everychapterhasbeencriticallyupdated,andtherearenewchaptersonkeytechnologies.You’llbeabletolearnandworkwithTensorFlowmoredeeplythaneverbefore,andgetessentialcoverageoftheKerasneuralnetworklibrary,alongwiththemostrecentupdatestoscikit-learn.StyleandApproachPythonMachineLearningSecondEditiontakesapractical,hands-oncodingapproachsoyoucanlearnaboutmachinelearningbycodingwithPython.ThisbookmovesfluentlybetweenthetheoreticalprinciplesofmachinelearningandthepracticaldetailsofimplementationwithPython.

Sebastian Raschka Vahid Mirjalili ·程序設計 ·13.3萬字

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