ScientificVisualizationwithwxPythonandMatplotlib使用的wxpython与matplotlib的科学可视化ScottPearseCSCI5448Spring2011上的讲义ppt的pdf版。
简要表述了如何使用着两者，并同时使用以及效果图等内容。
简要表述了如何使用着两者，并同时使用以及效果图等内容。
2025/5/20 3:27:07
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EPSON爱普生PX-M5041FWF-7620WF-7621A3一体机拆机图解
2025/5/20 1:21:49
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《世界著名三角学经典著作钩沉》是2010年哈尔滨工业大学出版社出版的图书，由《世界名三角学经典作钩沉》编写组编著。
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TOPICSChapters1-6providecoverageofappliedlinearalgebrasufficientforreadingtheremainderofthebook.Chapter1:MatricesThechapterintroducesmatrixarithmeticandtheveryimportanttopicoflineartransformations.Rotationmatricesprovideaninterestingandusefulexampleoflineartransformations.Afterdiscussingmatrixpowers,theconceptofthematrixinverseandtransposeconcludesthechapter.Chapter2:LinearEquationsThischapterintroducesGaussianeliminationforthesolutionoflinearsystemsAx=bandforthecomputationofthematrixinverse.Thechapteralsointroducestherelationshipbetweenthematrixinverseandthesolutiontoalinearhomogeneousequation.Twoapplicationsinvolvingatrussandanelectricalcircuitconcludethechapter.Chapter3:SubspacesThischapteris,byitsverynature,somewhatabstract.Itintroducestheconceptsofsubspaces,linearindependence,basis,matrixrank,range,andnullspace.Althoughthechaptermaychallengesomereaders,theconceptsareessentialforunderstandingmanytopicsinthebook,anditshouldbecoveredthoroughly.Chapter4:DeterminantsAlthoughthedeterminantisrarelycomputedinpractice,itisoftenusedinproofsofimportantresults.Thechapterintroducesthedeterminantanditscomputationusingexpansionbyminorsandbyrowelimination.Thechapterendswithaninterestingapplicationofthedeterminanttotextencryption.Chapter5:EigenvaluesandEigenvectorsThisisaveryimportantchapter,anditsresultsareusedthroughoutthebook.Afterdefiningtheeigenvalueandanassociatedeigenvector,thechapterdevelopssomeoftheirmostimportantproperties,includingtheiruseinmatrixdiagonalization.Thechapterconcludeswithanapplicationtothesolutionofsystemsofordinarydifferentialequationsandtheproblemofrankingitemsusingeigenvectors.Chapter6:OrthogonalVectorsandMatricesThischapterintroducestheinnerproductanditsassociati
2025/5/19 18:06:43
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WhatisaNoisy-ORModelIaminterestedwiththepaper"Noisy-ORComponentAnalysisanditsApplicationtoLinkAnalysis"publishedbyTomasSingliarandMilosHauskrechtonJMLR7(2006).Averypreliminaryprerequisitetounderstandthispaperistoknowthe"noisy-or"model.However,itseemsthatnoisy-orisanoldtopicandnomuchcanbefoundviaGoogle.Fortunately,Igotaverybriefdescriptionfromanoldpaper"PossibilitytheoryandthegeneralizedNoisyORmodel".SnapshottingthesectiononNoisyORasanimageasattached:
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