Machine Learning - Stanford Online Course.torrent



Machine Learning - Stanford Online Course.torrent

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Machine Learning - Stanford Online Course.torrent


Torrent Contents

Machine Learning - Stanford
Self-notes
08-10.txt109 B
12-10.txt724 B
22-11.txt18 B
Lecture1.pdf4 MB
Lecture10.pdf1 MB
Lecture11.pdf497 KB
Lecture12.pdf2 MB
Lecture13.pdf2 MB
Lecture14.pdf1 MB
Lecture15.pdf3 MB
Lecture16.pdf1 MB
Lecture2.pdf2 MB
Lecture3.pdf1 MB
Lecture4.pdf1 MB
Lecture6.pdf1 MB
Lecture7.pdf1 MB
Lecture8.pdf5 MB
Lecture9.pdf3 MB
octave_session.m5 KB
01.2-V2-Introduction-WhatIsMachineLearning.mp430 MB
01.3-V2-Introduction-SupervisedLearning.mp415 MB
01.4-V2-Introduction-UnsupervisedLearning.mp438 MB
02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation.mp411 MB
02.2-V2-LinearRegressionWithOneVariable-CostFunction.mp413 MB
02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp416 MB
02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp431 MB
02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp426 MB
02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp418 MB
02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp425 MB
02.8-V2-What'sNext.mp47 MB
03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors.mp411 MB
03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication.mp49 MB
03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication.mp420 MB
03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication.mp422 MB
03.5-V2-LinearAlgebraReview(Optional)-MatrixMultiplicationProperties.mp411 MB
03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp424 MB
04.1-LinearRegressionWithMultipleVariables-MultipleFeatures.mp46 MB
04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables.mp45 MB
04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling.mp47 MB
04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate.mp46 MB
04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression.mp45 MB
04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation.mp413 MB
04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional).mp45 MB
05.1-OctaveTutorial-BasicOperations.mp420 MB
05.2-OctaveTutorial-MovingDataAround.mp425 MB
05.3-OctaveTutorial-ComputingOnData.mp410 MB
05.4-OctaveTutorial-PlottingData.mp411 MB
05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions.mp419 MB
05.6-OctaveTutorial-Vectorization.mp416 MB
05.7-OctaveTutorial-WorkingOnAndSubmittingProgrammingExercises.mp47 MB
06.1-LogisticRegression-Classification.mp48 MB
06.2-LogisticRegression-HypothesisRepresentation.mp48 MB
06.3-LogisticRegression-DecisionBoundary.mp417 MB
06.4-LogisticRegression-CostFunction.mp414 MB
06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent.mp413 MB
06.6-LogisticRegression-AdvancedOptimization.mp421 MB
06.7-LogisticRegression-MultiClassClassificationOneVsAll.mp47 MB
07.1-Regularization-TheProblemOfOverfitting.mp411 MB
07.2-Regularization-CostFunction.mp412 MB
07.3-Regularization-RegularizedLinearRegression.mp412 MB
07.4-Regularization-RegularizedLogisticRegression.mp413 MB
08.1-NeuralNetworksRepresentation-NonLinearHypotheses.mp411 MB
08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain.mp411 MB
08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp414 MB
08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp414 MB
08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI.mp48 MB
08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp416 MB
08.7-NeuralNetworksRepresentation-MultiClassClassification.mp45 MB
09.1-NeuralNetworksLearning-CostFunction.mp48 MB
09.2-NeuralNetworksLearning-BackpropagationAlgorithm.mp415 MB
09.3-NeuralNetworksLearning-BackpropagationIntuition.mp417 MB
09.3-NeuralNetworksLearning-ImplementationNoteUnrollingParameters.mp410 MB
09.4-NeuralNetworksLearning-GradientChecking.mp414 MB
09.5-NeuralNetworksLearning-RandomInitialization.mp47 MB
09.7-NeuralNetworksLearning-PuttingItTogether.mp417 MB
09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp421 MB
10.1-AdviceForApplyingMachineLearning-DecidingWhatToTryNext.mp47 MB
10.2-AdviceForApplyingMachineLearning-EvaluatingAHypothesis.mp49 MB
10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp416 MB
10.4-AdviceForApplyingMachineLearning-DiagnosingBiasVsVariance.mp410 MB
10.5-AdviceForApplyingMachineLearning-RegularizationAndBiasVariance.mp413 MB
10.6-AdviceForApplyingMachineLearning-LearningCurves.mp413 MB
10.7-AdviceForApplyingMachineLearning-DecidingWhatToDoNextRevisited.mp48 MB
11.1-MachineLearningSystemDesign-PrioritizingWhatToWorkOn.mp412 MB
11.2-MachineLearningSystemDesign-ErrorAnalysis.mp416 MB
11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp414 MB
11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp417 MB
11.5-MachineLearningSystemDesign-DataForMachineLearning.mp413 MB
12.1-SupportVectorMachines-OptimizationObjective.mp417 MB
12.2-SupportVectorMachines-LargeMarginIntuition.mp412 MB
12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp422 MB
12.4-SupportVectorMachines-KernelsI.mp418 MB
12.5-SupportVectorMachines-KernelsII.mp418 MB
12.6-SupportVectorMachines-UsingAnSVM.mp425 MB
14.1-Clustering-UnsupervisedLearningIntroduction.mp44 MB
14.2-Clustering-KMeansAlgorithm.mp414 MB
14.3-Clustering-OptimizationObjective.mp48 MB
14.4-Clustering-RandomInitialization.mp49 MB
14.5-Clustering-ChoosingTheNumberOfClusters.mp410 MB
15.1-DimensionalityReduction-MotivationIDataCompression.mp417 MB
15.2-DimensionalityReduction-MotivationIIVisualization.mp46 MB
15.3-DimensionalityReduction-PrincipalComponentAnalysisProblemFormulation.mp411 MB
15.4-DimensionalityReduction-PrincipalComponentAnalysisAlgorithm.mp419 MB
15.5-DimensionalityReduction-ChoosingTheNumberOfPrincipalComponents.mp412 MB
15.6-DimensionalityReduction-ReconstructionFromCompressedRepresentation.mp45 MB
15.7-DimensionalityReduction-AdviceForApplyingPCA.mp415 MB
16.1-AnomalyDetection-ProblemMotivation-V1.mp48 MB
16.2-AnomalyDetection-GaussianDistribution.mp412 MB
16.3-AnomalyDetection-Algorithm.mp415 MB
16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp416 MB
16.5-AnomalyDetection-AnomalyDetectionVsSupervisedLearning-V1.mp410 MB
16.6-AnomalyDetection-ChoosingWhatFeaturesToUse.mp415 MB
16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp417 MB
16.8-AnomalyDetection-AnomalyDetectionUsing...ltivariateGaussianDistribution-OPTIONAL.mp417 MB
17.1-RecommenderSystems-ProblemFormulation.mp413 MB
17.2-RecommenderSystems-ContentBasedRecommendations.mp418 MB
17.3-RecommenderSystems-CollaborativeFiltering-V1.mp413 MB
17.4-RecommenderSystems-CollaborativeFilteringAlgorithm.mp411 MB
17.5-RecommenderSystems-VectorizationLowRankMatrixFactorization.mp410 MB
17.6-RecommenderSystems-ImplementationalDetailMeanNormalization.mp410 MB
18.1-LargeScaleMachineLearning-LearningWithLargeDatasets.mp47 MB
18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp416 MB
18.3-LargeScaleMachineLearning-MiniBatchGradientDescent.mp47 MB
18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp414 MB
18.5-LargeScaleMachineLearning-OnlineLearning.mp415 MB
18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp417 MB
19.1-ApplicationExamplePhotoOCR-ProblemDescriptionAndPipeline.mp48 MB
19.2-ApplicationExamplePhotoOCR-SlidingWindows.mp410 MB
19.3-ApplicationExamplePhotoOCR-GettingLotsOfDataArtificialDataSynthesis.mp48 MB
19.4-ApplicationExamplePhotoOCR-CeilingAnalysisWhatPartOfThePipelineToWorkOnNext.mp410 MB
20.1-Conclusion-SummaryAndThankYou.mp44 MB
Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe69 MB


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