Machine Learning - Stanford Online Course.torrent

 

 

 

 

 

 

 

Machine Learning - Stanford
Self-notes
08-10.txt 109 B
12-10.txt 724 B
22-11.txt 18 B
Lecture1.pdf 4 MB
Lecture10.pdf 1 MB
Lecture11.pdf 497 KB
Lecture12.pdf 2 MB
Lecture13.pdf 2 MB
Lecture14.pdf 1 MB
Lecture15.pdf 3 MB
Lecture16.pdf 1 MB
Lecture2.pdf 2 MB
Lecture3.pdf 1 MB
Lecture4.pdf 1 MB
Lecture6.pdf 1 MB
Lecture7.pdf 1 MB
Lecture8.pdf 5 MB
Lecture9.pdf 3 MB
octave_session.m 5 KB
01.2-V2-Introduction-WhatIsMachineLearning.mp4 30 MB
01.3-V2-Introduction-SupervisedLearning.mp4 15 MB
01.4-V2-Introduction-UnsupervisedLearning.mp4 38 MB
02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation.mp4 11 MB
02.2-V2-LinearRegressionWithOneVariable-CostFunction.mp4 13 MB
02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp4 16 MB
02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp4 31 MB
02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp4 26 MB
02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp4 18 MB
02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp4 25 MB
02.8-V2-What'sNext.mp4 7 MB
03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors.mp4 11 MB
03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication.mp4 9 MB
03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication.mp4 20 MB
03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication.mp4 22 MB
03.5-V2-LinearAlgebraReview(Optional)-MatrixMultiplicationProperties.mp4 11 MB
03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp4 24 MB
04.1-LinearRegressionWithMultipleVariables-MultipleFeatures.mp4 6 MB
04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables.mp4 5 MB
04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling.mp4 7 MB
04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate.mp4 6 MB
04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression.mp4 5 MB
04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation.mp4 13 MB
04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional).mp4 5 MB
05.1-OctaveTutorial-BasicOperations.mp4 20 MB
05.2-OctaveTutorial-MovingDataAround.mp4 25 MB
05.3-OctaveTutorial-ComputingOnData.mp4 10 MB
05.4-OctaveTutorial-PlottingData.mp4 11 MB
05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions.mp4 19 MB
05.6-OctaveTutorial-Vectorization.mp4 16 MB
05.7-OctaveTutorial-WorkingOnAndSubmittingProgrammingExercises.mp4 7 MB
06.1-LogisticRegression-Classification.mp4 8 MB
06.2-LogisticRegression-HypothesisRepresentation.mp4 8 MB
06.3-LogisticRegression-DecisionBoundary.mp4 17 MB
06.4-LogisticRegression-CostFunction.mp4 14 MB
06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent.mp4 13 MB
06.6-LogisticRegression-AdvancedOptimization.mp4 21 MB
06.7-LogisticRegression-MultiClassClassificationOneVsAll.mp4 7 MB
07.1-Regularization-TheProblemOfOverfitting.mp4 11 MB
07.2-Regularization-CostFunction.mp4 12 MB
07.3-Regularization-RegularizedLinearRegression.mp4 12 MB
07.4-Regularization-RegularizedLogisticRegression.mp4 13 MB
08.1-NeuralNetworksRepresentation-NonLinearHypotheses.mp4 11 MB
08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain.mp4 11 MB
08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp4 14 MB
08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp4 14 MB
08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI.mp4 8 MB
08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp4 16 MB
08.7-NeuralNetworksRepresentation-MultiClassClassification.mp4 5 MB
09.1-NeuralNetworksLearning-CostFunction.mp4 8 MB
09.2-NeuralNetworksLearning-BackpropagationAlgorithm.mp4 15 MB
09.3-NeuralNetworksLearning-BackpropagationIntuition.mp4 17 MB
09.3-NeuralNetworksLearning-ImplementationNoteUnrollingParameters.mp4 10 MB
09.4-NeuralNetworksLearning-GradientChecking.mp4 14 MB
09.5-NeuralNetworksLearning-RandomInitialization.mp4 7 MB
09.7-NeuralNetworksLearning-PuttingItTogether.mp4 17 MB
09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp4 21 MB
10.1-AdviceForApplyingMachineLearning-DecidingWhatToTryNext.mp4 7 MB
10.2-AdviceForApplyingMachineLearning-EvaluatingAHypothesis.mp4 9 MB
10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp4 16 MB
10.4-AdviceForApplyingMachineLearning-DiagnosingBiasVsVariance.mp4 10 MB
10.5-AdviceForApplyingMachineLearning-RegularizationAndBiasVariance.mp4 13 MB
10.6-AdviceForApplyingMachineLearning-LearningCurves.mp4 13 MB
10.7-AdviceForApplyingMachineLearning-DecidingWhatToDoNextRevisited.mp4 8 MB
11.1-MachineLearningSystemDesign-PrioritizingWhatToWorkOn.mp4 12 MB
11.2-MachineLearningSystemDesign-ErrorAnalysis.mp4 16 MB
11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp4 14 MB
11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp4 17 MB
11.5-MachineLearningSystemDesign-DataForMachineLearning.mp4 13 MB
12.1-SupportVectorMachines-OptimizationObjective.mp4 17 MB
12.2-SupportVectorMachines-LargeMarginIntuition.mp4 12 MB
12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp4 22 MB
12.4-SupportVectorMachines-KernelsI.mp4 18 MB
12.5-SupportVectorMachines-KernelsII.mp4 18 MB
12.6-SupportVectorMachines-UsingAnSVM.mp4 25 MB
14.1-Clustering-UnsupervisedLearningIntroduction.mp4 4 MB
14.2-Clustering-KMeansAlgorithm.mp4 14 MB
14.3-Clustering-OptimizationObjective.mp4 8 MB
14.4-Clustering-RandomInitialization.mp4 9 MB
14.5-Clustering-ChoosingTheNumberOfClusters.mp4 10 MB
15.1-DimensionalityReduction-MotivationIDataCompression.mp4 17 MB
15.2-DimensionalityReduction-MotivationIIVisualization.mp4 6 MB
15.3-DimensionalityReduction-PrincipalComponentAnalysisProblemFormulation.mp4 11 MB
15.4-DimensionalityReduction-PrincipalComponentAnalysisAlgorithm.mp4 19 MB
15.5-DimensionalityReduction-ChoosingTheNumberOfPrincipalComponents.mp4 12 MB
15.6-DimensionalityReduction-ReconstructionFromCompressedRepresentation.mp4 5 MB
15.7-DimensionalityReduction-AdviceForApplyingPCA.mp4 15 MB
16.1-AnomalyDetection-ProblemMotivation-V1.mp4 8 MB
16.2-AnomalyDetection-GaussianDistribution.mp4 12 MB
16.3-AnomalyDetection-Algorithm.mp4 15 MB
16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp4 16 MB
16.5-AnomalyDetection-AnomalyDetectionVsSupervisedLearning-V1.mp4 10 MB
16.6-AnomalyDetection-ChoosingWhatFeaturesToUse.mp4 15 MB
16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp4 17 MB
16.8-AnomalyDetection-AnomalyDetectionUsing...ltivariateGaussianDistribution-OPTIONAL.mp4 17 MB
17.1-RecommenderSystems-ProblemFormulation.mp4 13 MB
17.2-RecommenderSystems-ContentBasedRecommendations.mp4 18 MB
17.3-RecommenderSystems-CollaborativeFiltering-V1.mp4 13 MB
17.4-RecommenderSystems-CollaborativeFilteringAlgorithm.mp4 11 MB
17.5-RecommenderSystems-VectorizationLowRankMatrixFactorization.mp4 10 MB
17.6-RecommenderSystems-ImplementationalDetailMeanNormalization.mp4 10 MB
18.1-LargeScaleMachineLearning-LearningWithLargeDatasets.mp4 7 MB
18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp4 16 MB
18.3-LargeScaleMachineLearning-MiniBatchGradientDescent.mp4 7 MB
18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp4 14 MB
18.5-LargeScaleMachineLearning-OnlineLearning.mp4 15 MB
18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp4 17 MB
19.1-ApplicationExamplePhotoOCR-ProblemDescriptionAndPipeline.mp4 8 MB
19.2-ApplicationExamplePhotoOCR-SlidingWindows.mp4 10 MB
19.3-ApplicationExamplePhotoOCR-GettingLotsOfDataArtificialDataSynthesis.mp4 8 MB
19.4-ApplicationExamplePhotoOCR-CeilingAnalysisWhatPartOfThePipelineToWorkOnNext.mp4 10 MB
20.1-Conclusion-SummaryAndThankYou.mp4 4 MB
Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe 69 MB

 

 

 

 

 

 

 

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