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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

大話統計學(溢彩實訓版):基于R語言+中文統計工具
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“統計學”是兼具數學計算與圖形可視化的課程,也是大數據、運籌學、數據挖掘、數據科學、統計計算、統計學習、機器學習、深度學習和人工智能等技術的基礎學科。目前學習統計學的難點在于先學理論還是先學統計工具。統計學的理論體系龐雜而精密,學習起來枯燥艱辛;而如果從統計工具應用上手,則會有其他問題出現,所有的統計軟件(如SPSS),并非計算機輔助教學(CAI),它們并非“教你學會統計”,而是應該在“學會了統計”以后,再來用它。針對這個矛盾,《大話統計學溢彩實訓版:基于R語言+中文統計工具》將從零開始接觸統計學,利用自行開發的統計小工具配合理論學習,將其真正應用到工作中,穩步跟進大數據時代。《大話統計學溢彩實訓版:基于R語言+中文統計工具》內容前后連貫——從概率到抽樣,從描述到推斷,從檢驗到因果。全書思路清晰,用大量圖表將內容貫穿到一起。形式上有引言、觀念圖、流程圖、思維導圖、階層圖、分類圖、關聯圖、步驟圖、流程圖,以及因果表、比較表、決策法則表等。《大話統計學溢彩實訓版:基于R語言+中文統計工具》因圖表和數據可視化的需要,采用彩色印刷,不僅可以更好地展示統計圖表細節,更讓《大話統計學溢彩實訓版:基于R語言+中文統計工具》的各類流程圖、思維導圖等學習圖譜更精準易讀。

陳文賢 ·程序設計 ·13.3萬字

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