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种子名称： Machine Learning - Stanford
文件类型：
文件数目： 113个文件
文件大小： 1.62 GB
收录时间： 2011-12-28 18:22
已经下载： 3次
资源热度： 68
最近下载： 2024-12-14 08:58

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Machine Learning - Stanford.torrent

01.2-V2-Introduction-WhatIsMachineLearning.mp430.4MB

01.3-V2-Introduction-SupervisedLearning.mp415.45MB

01.4-V2-Introduction-UnsupervisedLearning.mp438.56MB

02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation.mp411.61MB

02.2-V2-LinearRegressionWithOneVariable-CostFunction.mp413.08MB

02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp416.3MB

02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp431.62MB

02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp427MB

02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp418.92MB

02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp425.47MB

02.8-V2-What'sNext.mp47.3MB

03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors.mp411.92MB

03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication.mp49.22MB

03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication.mp420.23MB

03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication.mp422.35MB

03.5-V2-LinearAlgebraReview(Optional)-MatrixMultiplicationProperties.mp411.77MB

03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp424.6MB

04.1-LinearRegressionWithMultipleVariables-MultipleFeatures.mp46.13MB

04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables.mp45.9MB

04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling.mp47.58MB

04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate.mp46.86MB

04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression.mp45.74MB

04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation.mp413.34MB

04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional).mp45.18MB

05.1-OctaveTutorial-BasicOperations.mp420.69MB

05.2-OctaveTutorial-MovingDataAround.mp425.42MB

05.3-OctaveTutorial-ComputingOnData.mp410.37MB

05.4-OctaveTutorial-PlottingData.mp411.31MB

05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions.mp419.69MB

05.6-OctaveTutorial-Vectorization.mp416.83MB

05.7-OctaveTutorial-WorkingOnAndSubmittingProgrammingExercises.mp47.25MB

06.1-LogisticRegression-Classification.mp48.73MB

06.2-LogisticRegression-HypothesisRepresentation.mp48.81MB

06.3-LogisticRegression-DecisionBoundary.mp417.51MB

06.4-LogisticRegression-CostFunction.mp414.11MB

06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent.mp413.07MB

06.6-LogisticRegression-AdvancedOptimization.mp421.59MB

06.7-LogisticRegression-MultiClassClassificationOneVsAll.mp47.29MB

07.1-Regularization-TheProblemOfOverfitting.mp411.96MB

07.2-Regularization-CostFunction.mp412.43MB

07.3-Regularization-RegularizedLinearRegression.mp412.77MB

07.4-Regularization-RegularizedLogisticRegression.mp413.5MB

08.1-NeuralNetworksRepresentation-NonLinearHypotheses.mp411.53MB

08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain.mp411.47MB

08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp414.37MB

08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp414.41MB

08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI.mp48.29MB

08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp416.84MB

08.7-NeuralNetworksRepresentation-MultiClassClassification.mp45.41MB

09.1-NeuralNetworksLearning-CostFunction.mp48.1MB

09.2-NeuralNetworksLearning-BackpropagationAlgorithm.mp415.07MB

09.3-NeuralNetworksLearning-BackpropagationIntuition.mp417.14MB

09.3-NeuralNetworksLearning-ImplementationNoteUnrollingParameters.mp410.54MB

09.4-NeuralNetworksLearning-GradientChecking.mp414.76MB

09.5-NeuralNetworksLearning-RandomInitialization.mp47.95MB

09.7-NeuralNetworksLearning-PuttingItTogether.mp417.88MB

09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp421.25MB

10.1-AdviceForApplyingMachineLearning-DecidingWhatToTryNext.mp47.58MB

10.2-AdviceForApplyingMachineLearning-EvaluatingAHypothesis.mp49.52MB

10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp416.13MB

10.4-AdviceForApplyingMachineLearning-DiagnosingBiasVsVariance.mp410.42MB

10.5-AdviceForApplyingMachineLearning-RegularizationAndBiasVariance.mp413.87MB

10.6-AdviceForApplyingMachineLearning-LearningCurves.mp413.54MB

10.7-AdviceForApplyingMachineLearning-DecidingWhatToDoNextRevisited.mp48.94MB

11.1-MachineLearningSystemDesign-PrioritizingWhatToWorkOn.mp412.32MB

11.2-MachineLearningSystemDesign-ErrorAnalysis.mp416.94MB

11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp414.24MB

11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp417.29MB

11.5-MachineLearningSystemDesign-DataForMachineLearning.mp413.98MB

12.1-SupportVectorMachines-OptimizationObjective.mp417.77MB

12.2-SupportVectorMachines-LargeMarginIntuition.mp412.66MB

12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp422.91MB

12.4-SupportVectorMachines-KernelsI.mp418.74MB

12.5-SupportVectorMachines-KernelsII.mp418.31MB

12.6-SupportVectorMachines-UsingAnSVM.mp425.76MB

14.1-Clustering-UnsupervisedLearningIntroduction.mp44.12MB

14.2-Clustering-KMeansAlgorithm.mp414.67MB

14.3-Clustering-OptimizationObjective.mp48.78MB

14.4-Clustering-RandomInitialization.mp49.31MB

14.5-Clustering-ChoosingTheNumberOfClusters.mp410.11MB

15.1-DimensionalityReduction-MotivationIDataCompression.mp417.63MB

15.2-DimensionalityReduction-MotivationIIVisualization.mp46.91MB

15.3-DimensionalityReduction-PrincipalComponentAnalysisProblemFormulation.mp411.4MB

15.4-DimensionalityReduction-PrincipalComponentAnalysisAlgorithm.mp419.4MB

15.5-DimensionalityReduction-ChoosingTheNumberOfPrincipalComponents.mp412.47MB

15.6-DimensionalityReduction-ReconstructionFromCompressedRepresentation.mp45.93MB

15.7-DimensionalityReduction-AdviceForApplyingPCA.mp415.8MB

16.1-AnomalyDetection-ProblemMotivation-V1.mp48.83MB

16.2-AnomalyDetection-GaussianDistribution.mp412.88MB

16.3-AnomalyDetection-Algorithm.mp415.3MB

16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp416.92MB

16.5-AnomalyDetection-AnomalyDetectionVsSupervisedLearning-V1.mp410.79MB

16.6-AnomalyDetection-ChoosingWhatFeaturesToUse.mp415.43MB

16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp417.27MB

16.8-AnomalyDetection-AnomalyDetectionUsingTheMultivariateGaussianDistribution-OPTIONAL.mp417.75MB

17.1-RecommenderSystems-ProblemFormulation.mp413.65MB

17.2-RecommenderSystems-ContentBasedRecommendations.mp418.73MB

17.3-RecommenderSystems-CollaborativeFiltering-V1.mp413.1MB

17.4-RecommenderSystems-CollaborativeFilteringAlgorithm.mp411.44MB

17.5-RecommenderSystems-VectorizationLowRankMatrixFactorization.mp410.45MB

17.6-RecommenderSystems-ImplementationalDetailMeanNormalization.mp410.46MB

18.1-LargeScaleMachineLearning-LearningWithLargeDatasets.mp47.12MB

18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp416.4MB

18.3-LargeScaleMachineLearning-MiniBatchGradientDescent.mp47.99MB

18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp414.41MB

18.5-LargeScaleMachineLearning-OnlineLearning.mp415.96MB

18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp417.3MB

19.1-ApplicationExamplePhotoOCR-ProblemDescriptionAndPipeline.mp48.54MB

19.2-ApplicationExamplePhotoOCR-SlidingWindows.mp410.1MB

19.3-ApplicationExamplePhotoOCR-GettingLotsOfDataArtificialDataSynthesis.mp48.53MB

19.4-ApplicationExamplePhotoOCR-CeilingAnalysisWhatPartOfThePipelineToWorkOnNext.mp410.64MB

20.1-Conclusion-SummaryAndThankYou.mp44.52MB

Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe69.61MB