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Torrent Details
Machine Learning - Stanford
Category:
Other
Size:
1.62 GB
Seeders:
8
Leechers:
1
Downloads:
19
Uploaded:
Oct 20, 2018
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Machine Learning - Stanford
Self-notes
08-10.txt
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12-10.txt
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22-11.txt
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Lecture1.pdf
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Lecture10.pdf
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Lecture11.pdf
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Lecture12.pdf
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Lecture13.pdf
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Lecture14.pdf
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Lecture15.pdf
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Lecture16.pdf
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Lecture2.pdf
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Lecture3.pdf
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Lecture4.pdf
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Lecture6.pdf
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Lecture7.pdf
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Lecture8.pdf
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Lecture9.pdf
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octave_session.m
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01.2-V2-Introduction-WhatIsMachineLearning.mp4
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01.3-V2-Introduction-SupervisedLearning.mp4
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01.4-V2-Introduction-UnsupervisedLearning.mp4
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02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation.mp4
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02.2-V2-LinearRegressionWithOneVariable-CostFunction.mp4
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02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp4
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02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp4
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02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp4
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02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp4
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02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp4
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02.8-V2-What'sNext.mp4
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03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors.mp4
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03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication.mp4
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03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication.mp4
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03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication.mp4
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03.5-V2-LinearAlgebraReview(Optional)-MatrixMultiplicationProperties.mp4
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03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp4
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04.1-LinearRegressionWithMultipleVariables-MultipleFeatures.mp4
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04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables.mp4
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04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling.mp4
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04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate.mp4
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04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression.mp4
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04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation.mp4
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04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional).mp4
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05.1-OctaveTutorial-BasicOperations.mp4
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05.2-OctaveTutorial-MovingDataAround.mp4
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05.3-OctaveTutorial-ComputingOnData.mp4
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05.4-OctaveTutorial-PlottingData.mp4
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05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions.mp4
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05.6-OctaveTutorial-Vectorization.mp4
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05.7-OctaveTutorial-WorkingOnAndSubmittingProgrammingExercises.mp4
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06.1-LogisticRegression-Classification.mp4
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06.2-LogisticRegression-HypothesisRepresentation.mp4
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06.3-LogisticRegression-DecisionBoundary.mp4
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06.4-LogisticRegression-CostFunction.mp4
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06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent.mp4
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06.6-LogisticRegression-AdvancedOptimization.mp4
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06.7-LogisticRegression-MultiClassClassificationOneVsAll.mp4
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07.1-Regularization-TheProblemOfOverfitting.mp4
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07.2-Regularization-CostFunction.mp4
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07.3-Regularization-RegularizedLinearRegression.mp4
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07.4-Regularization-RegularizedLogisticRegression.mp4
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08.1-NeuralNetworksRepresentation-NonLinearHypotheses.mp4
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08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain.mp4
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08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp4
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08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp4
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08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI.mp4
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08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp4
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08.7-NeuralNetworksRepresentation-MultiClassClassification.mp4
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09.1-NeuralNetworksLearning-CostFunction.mp4
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09.2-NeuralNetworksLearning-BackpropagationAlgorithm.mp4
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09.3-NeuralNetworksLearning-BackpropagationIntuition.mp4
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09.3-NeuralNetworksLearning-ImplementationNoteUnrollingParameters.mp4
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09.4-NeuralNetworksLearning-GradientChecking.mp4
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09.5-NeuralNetworksLearning-RandomInitialization.mp4
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09.7-NeuralNetworksLearning-PuttingItTogether.mp4
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09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp4
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10.1-AdviceForApplyingMachineLearning-DecidingWhatToTryNext.mp4
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10.2-AdviceForApplyingMachineLearning-EvaluatingAHypothesis.mp4
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10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp4
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10.4-AdviceForApplyingMachineLearning-DiagnosingBiasVsVariance.mp4
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10.5-AdviceForApplyingMachineLearning-RegularizationAndBiasVariance.mp4
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10.6-AdviceForApplyingMachineLearning-LearningCurves.mp4
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10.7-AdviceForApplyingMachineLearning-DecidingWhatToDoNextRevisited.mp4
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11.1-MachineLearningSystemDesign-PrioritizingWhatToWorkOn.mp4
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11.2-MachineLearningSystemDesign-ErrorAnalysis.mp4
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11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp4
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11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp4
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11.5-MachineLearningSystemDesign-DataForMachineLearning.mp4
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12.1-SupportVectorMachines-OptimizationObjective.mp4
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12.2-SupportVectorMachines-LargeMarginIntuition.mp4
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12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp4
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12.4-SupportVectorMachines-KernelsI.mp4
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12.5-SupportVectorMachines-KernelsII.mp4
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12.6-SupportVectorMachines-UsingAnSVM.mp4
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14.1-Clustering-UnsupervisedLearningIntroduction.mp4
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14.2-Clustering-KMeansAlgorithm.mp4
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14.3-Clustering-OptimizationObjective.mp4
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14.4-Clustering-RandomInitialization.mp4
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14.5-Clustering-ChoosingTheNumberOfClusters.mp4
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15.1-DimensionalityReduction-MotivationIDataCompression.mp4
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15.2-DimensionalityReduction-MotivationIIVisualization.mp4
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15.3-DimensionalityReduction-PrincipalComponentAnalysisProblemFormulation.mp4
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15.4-DimensionalityReduction-PrincipalComponentAnalysisAlgorithm.mp4
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15.5-DimensionalityReduction-ChoosingTheNumberOfPrincipalComponents.mp4
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15.6-DimensionalityReduction-ReconstructionFromCompressedRepresentation.mp4
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15.7-DimensionalityReduction-AdviceForApplyingPCA.mp4
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16.1-AnomalyDetection-ProblemMotivation-V1.mp4
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16.2-AnomalyDetection-GaussianDistribution.mp4
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16.3-AnomalyDetection-Algorithm.mp4
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16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp4
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16.5-AnomalyDetection-AnomalyDetectionVsSupervisedLearning-V1.mp4
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16.6-AnomalyDetection-ChoosingWhatFeaturesToUse.mp4
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16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp4
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16.8-AnomalyDetection-AnomalyDetectionUsingTheMultivariateGaussianDistribution-OPTIONAL.mp4
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17.1-RecommenderSystems-ProblemFormulation.mp4
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17.2-RecommenderSystems-ContentBasedRecommendations.mp4
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17.3-RecommenderSystems-CollaborativeFiltering-V1.mp4
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17.4-RecommenderSystems-CollaborativeFilteringAlgorithm.mp4
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17.5-RecommenderSystems-VectorizationLowRankMatrixFactorization.mp4
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17.6-RecommenderSystems-ImplementationalDetailMeanNormalization.mp4
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18.1-LargeScaleMachineLearning-LearningWithLargeDatasets.mp4
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18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp4
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18.3-LargeScaleMachineLearning-MiniBatchGradientDescent.mp4
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18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp4
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18.5-LargeScaleMachineLearning-OnlineLearning.mp4
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18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp4
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19.1-ApplicationExamplePhotoOCR-ProblemDescriptionAndPipeline.mp4
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19.2-ApplicationExamplePhotoOCR-SlidingWindows.mp4
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19.3-ApplicationExamplePhotoOCR-GettingLotsOfDataArtificialDataSynthesis.mp4
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19.4-ApplicationExamplePhotoOCR-CeilingAnalysisWhatPartOfThePipelineToWorkOnNext.mp4
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20.1-Conclusion-SummaryAndThankYou.mp4
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Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe
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InfoHash:
3B33CB59C493ED3468B81E2DD388419482C16F39
Last updated:
Jan 24, 2022
File count:
132 B
File Category:
1
Tags:
machine, learning, stanford
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