Author:MichaelE.Whitman,MichaelE.WhitmanThepurposeofthistextbookistofulfilltheneedforaqualityacademictextbookinthedisciplineofinformationsecuritymanagement.

《SciPyandNumPy》中文精简版.7z

Python科学计算最佳实践——SciPy指南(ElegantSciPy)，包含代码和数据源

Thistextbookintroducessparseandredundantrepresentationswithafocusonapplicationsinsignalandimageprocessing.Thetheoreticalandnumericalfoundationsaretackledbeforetheapplicationsarediscussed.Mathematicalmodelingforsignalsourcesisdiscussedalongwithhowtousethepropermodelfortaskssuchasdenoising,restoration,separation,interpolationandextrapolation,compression,sampling,analysisandsynthesis,detection,recognition,andmore.Thepresentationiselegantandengaging.SparseandRedundantRepresentationsisintendedforgraduatestudentsinappliedmathematicsandelectricalengineering,aswellasappliedmathematicians,engineers,andresearcherswhoareactiveinthefieldsofsignalandimageprocessing.*Introducestheoreticalandnumericalfoundationsbeforetacklingapplications*Discusseshowtousethepropermodelforvarioussituations*Introducessparseandredundantrepresentations*FocusesonapplicationsinsignalandimageprocessingThefieldofsparseandredundantrepresentationmodelinghasgonethroughamajorrevolutioninthepasttwodecades.Thisstartedwithaseriesofalgorithmsforapproximatingthesparsestsolutionsoflinearsystemsofequations,latertobefollowedbysurprisingtheoreticalresultsthatguaranteethesealgorithms’performance.Withthesecontributionsinplace,majorbarriersinmakingthismodelpracticalandapplicablewereremoved,andsparsityandredundancybecamecentral,leadingtostate-of-the-artresultsinvariousdisciplines.Oneofthemainbeneficiariesofthisprogressisthefieldofimageprocessing,wherethismodelhasbeenshowntoleadtounprecedentedperformanceinvariousapplications.Thisbookprovidesacomprehensiveviewofthetopicofsparseandredundantrepresentationmodeling,anditsuseinsignalandimageprocessing.Itoffersasystematicandorderedexposuretothetheoreticalfoundationsofthisdatamodel,thenumericalaspec

python2.7版本基于win64位的scipy包导入方法：在window中win+R打开cmd工具，粘贴以下内容d:\Python27\Scripts\pip.exeinstalld:\scipy-0.19.0-cp27-cp27m-win_amd64.whl注意：d:\Python27为python安装目录。
install的目录为您numpy存放的目录，建议不放在桌面

scikit-learnisaPythonmoduleformachinelearningbuiltontopofSciPyanddistributedunderthe3-ClauseBSDlicense.

安装scipy、gensim库的必备库，我试验了很多次，最后是和gensim-3.8.1-cp37-cp37m-win_amd64.whl、scipy-1.4.1-cp37-cp37m-win_amd64.whl这两个版本配合才安装成功的。

传统CS1课程的跨学科方法，其中强调了计算在其他学科中的作用，从材料科学到基因组学再到天体物理学再到网络系统。

Review,'Amust-readresourceforanyonewhoisseriousaboutembracingtheopportunityofbigdata.',--CraigVaughan,GlobalVicePresidentatSAP,'Thisbookgoesbeyonddataanalytics101.It'stheessentialguideforthoseofus(allofus?)whosebusinessesarebuiltontheubiquityofdataopportunitiesandthenewmandatefordata-drivendecision-making.',--TomPhillips,CEOofMedia6DegreesandFormerHeadofGoogleSearchandAnalytics,'Dataisthefoundationofnewwavesofproductivitygrowth,innovation,andrichercustomerinsight.Onlyrecentlyviewedbroadlyasasourceofcompetitiveadvantage,dealingwellwithdataisrapidlybecomingtablestakestostayinthegame.Theauthors'deepappliedexperiencemakesthisamustread--awindowintoyourcompetitor'sstrategy.',--AlanMurray,SerialEntrepreneur;PartneratCoriolisVentures,'Thistimelybooksaysoutloudwhathasfinallybecomeapparent:inthemodernworld,DataisBusiness,andyoucannolongerthinkbusinesswithoutthinkingdata.ReadthisbookandyouwillunderstandtheSciencebehindthinkingdata.',--RonBekkerman,ChiefDataOfficeratCarmelVentures,'Agreatbookforbusinessmanagerswholeadorinteractwithdatascientists,whowishtobetterunderstandtheprinciplesandalgorithmsavailablewithoutthetechnicaldetailsofsingle-disciplinarybooks.',--RonnyKohavi,PartnerArchitectatMicrosoftOnlineServicesDivision,AbouttheAuthor,FosterProvostisProfessorandNECFacultyFellowattheNYUSternSchoolofBusinesswhereheteachesintheMBA,BusinessAnalytics,andDataScienceprograms.Hisaward-winningresearchisreadandcitedbroadly.Prof.Provosthasco-foundedseveralsuccessfulcompaniesfocusingondatascienceformarketing.,TomFawcettholdsaPh.D.inmachinelearningandhasworkedinindustryR&DformorethantwodecadesforcompaniessuchasGTELaboratories,NYNEX/VerizonLabs,andHPLabs.Hispublishedworkhasbecomestandar

python版本的numpy+mkl和scipy，官方下载特别慢，所以放到csdn上供大家下载

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。