Machine Learning - Stanford

磁力链接/BT种子名称

Machine Learning - Stanford

磁力链接/BT种子简介

种子哈希：3b33cb59c493ed3468b81e2dd388419482c16f39
文件大小： 1.62G
已经下载：10623次
下载速度：极快
收录时间：2020-03-04
最近下载：2025-10-11

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磁力链接

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API Integration

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文件列表

  • Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe 73.0 MB
  • 01.4-V2-Introduction-UnsupervisedLearning.mp4 40.4 MB
  • 02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp4 33.2 MB
  • 01.2-V2-Introduction-WhatIsMachineLearning.mp4 31.9 MB
  • 02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp4 28.3 MB
  • 12.6-SupportVectorMachines-UsingAnSVM.mp4 27.0 MB
  • 02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp4 26.7 MB
  • 05.2-OctaveTutorial-MovingDataAround.mp4 26.7 MB
  • 03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp4 25.8 MB
  • 12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp4 24.0 MB
  • 03.4-V2-LinearAlgebraReview(Optional)-MatrixMatrixMultiplication.mp4 23.4 MB
  • 06.6-LogisticRegression-AdvancedOptimization.mp4 22.6 MB
  • 09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp4 22.3 MB
  • 05.1-OctaveTutorial-BasicOperations.mp4 21.7 MB
  • 03.3-V2-LinearAlgebraReview(Optional)-MatrixVectorMultiplication.mp4 21.2 MB
  • 05.5-OctaveTutorial-ForWhileIfStatementsAndFunctions.mp4 20.6 MB
  • 15.4-DimensionalityReduction-PrincipalComponentAnalysisAlgorithm.mp4 20.3 MB
  • 02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp4 19.8 MB
  • 12.4-SupportVectorMachines-KernelsI.mp4 19.7 MB
  • 17.2-RecommenderSystems-ContentBasedRecommendations.mp4 19.6 MB
  • 12.5-SupportVectorMachines-KernelsII.mp4 19.2 MB
  • 09.7-NeuralNetworksLearning-PuttingItTogether.mp4 18.7 MB
  • 12.1-SupportVectorMachines-OptimizationObjective.mp4 18.6 MB
  • 16.8-AnomalyDetection-AnomalyDetectionUsingTheMultivariateGaussianDistribution-OPTIONAL.mp4 18.6 MB
  • 15.1-DimensionalityReduction-MotivationIDataCompression.mp4 18.5 MB
  • 06.3-LogisticRegression-DecisionBoundary.mp4 18.4 MB
  • 18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp4 18.1 MB
  • 11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp4 18.1 MB
  • 16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp4 18.1 MB
  • 09.3-NeuralNetworksLearning-BackpropagationIntuition.mp4 18.0 MB
  • 11.2-MachineLearningSystemDesign-ErrorAnalysis.mp4 17.8 MB
  • 16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp4 17.7 MB
  • 08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp4 17.7 MB
  • 05.6-OctaveTutorial-Vectorization.mp4 17.6 MB
  • 18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp4 17.2 MB
  • 02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp4 17.1 MB
  • 10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp4 16.9 MB
  • 18.5-LargeScaleMachineLearning-OnlineLearning.mp4 16.7 MB
  • 15.7-DimensionalityReduction-AdviceForApplyingPCA.mp4 16.6 MB
  • 01.3-V2-Introduction-SupervisedLearning.mp4 16.2 MB
  • 16.6-AnomalyDetection-ChoosingWhatFeaturesToUse.mp4 16.2 MB
  • 16.3-AnomalyDetection-Algorithm.mp4 16.0 MB
  • 09.2-NeuralNetworksLearning-BackpropagationAlgorithm.mp4 15.8 MB
  • 09.4-NeuralNetworksLearning-GradientChecking.mp4 15.5 MB
  • 14.2-Clustering-KMeansAlgorithm.mp4 15.4 MB
  • 08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp4 15.1 MB
  • 18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp4 15.1 MB
  • 08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp4 15.1 MB
  • 11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp4 14.9 MB
  • 06.4-LogisticRegression-CostFunction.mp4 14.8 MB
  • 11.5-MachineLearningSystemDesign-DataForMachineLearning.mp4 14.7 MB
  • 10.5-AdviceForApplyingMachineLearning-RegularizationAndBiasVariance.mp4 14.5 MB
  • 17.1-RecommenderSystems-ProblemFormulation.mp4 14.3 MB
  • 10.6-AdviceForApplyingMachineLearning-LearningCurves.mp4 14.2 MB
  • 07.4-Regularization-RegularizedLogisticRegression.mp4 14.2 MB
  • 04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation.mp4 14.0 MB
  • 17.3-RecommenderSystems-CollaborativeFiltering-V1.mp4 13.7 MB
  • 02.2-V2-LinearRegressionWithOneVariable-CostFunction.mp4 13.7 MB
  • 06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent.mp4 13.7 MB
  • 16.2-AnomalyDetection-GaussianDistribution.mp4 13.5 MB
  • 07.3-Regularization-RegularizedLinearRegression.mp4 13.4 MB
  • 12.2-SupportVectorMachines-LargeMarginIntuition.mp4 13.3 MB
  • 15.5-DimensionalityReduction-ChoosingTheNumberOfPrincipalComponents.mp4 13.1 MB
  • 07.2-Regularization-CostFunction.mp4 13.0 MB
  • 11.1-MachineLearningSystemDesign-PrioritizingWhatToWorkOn.mp4 12.9 MB
  • 07.1-Regularization-TheProblemOfOverfitting.mp4 12.5 MB
  • 03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors.mp4 12.5 MB
  • 03.5-V2-LinearAlgebraReview(Optional)-MatrixMultiplicationProperties.mp4 12.3 MB
  • 02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation.mp4 12.2 MB
  • 08.1-NeuralNetworksRepresentation-NonLinearHypotheses.mp4 12.1 MB
  • 08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain.mp4 12.0 MB
  • 17.4-RecommenderSystems-CollaborativeFilteringAlgorithm.mp4 12.0 MB
  • 15.3-DimensionalityReduction-PrincipalComponentAnalysisProblemFormulation.mp4 12.0 MB
  • 05.4-OctaveTutorial-PlottingData.mp4 11.9 MB
  • 16.5-AnomalyDetection-AnomalyDetectionVsSupervisedLearning-V1.mp4 11.3 MB
  • 19.4-ApplicationExamplePhotoOCR-CeilingAnalysisWhatPartOfThePipelineToWorkOnNext.mp4 11.2 MB
  • 09.3-NeuralNetworksLearning-ImplementationNoteUnrollingParameters.mp4 11.1 MB
  • 17.6-RecommenderSystems-ImplementationalDetailMeanNormalization.mp4 11.0 MB
  • 17.5-RecommenderSystems-VectorizationLowRankMatrixFactorization.mp4 11.0 MB
  • 10.4-AdviceForApplyingMachineLearning-DiagnosingBiasVsVariance.mp4 10.9 MB
  • 05.3-OctaveTutorial-ComputingOnData.mp4 10.9 MB
  • 14.5-Clustering-ChoosingTheNumberOfClusters.mp4 10.6 MB
  • 19.2-ApplicationExamplePhotoOCR-SlidingWindows.mp4 10.6 MB
  • 10.2-AdviceForApplyingMachineLearning-EvaluatingAHypothesis.mp4 10.0 MB
  • 14.4-Clustering-RandomInitialization.mp4 9.8 MB
  • 03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication.mp4 9.7 MB
  • 10.7-AdviceForApplyingMachineLearning-DecidingWhatToDoNextRevisited.mp4 9.4 MB
  • 16.1-AnomalyDetection-ProblemMotivation-V1.mp4 9.3 MB
  • 06.2-LogisticRegression-HypothesisRepresentation.mp4 9.2 MB
  • 14.3-Clustering-OptimizationObjective.mp4 9.2 MB
  • 06.1-LogisticRegression-Classification.mp4 9.2 MB
  • 19.1-ApplicationExamplePhotoOCR-ProblemDescriptionAndPipeline.mp4 9.0 MB
  • 19.3-ApplicationExamplePhotoOCR-GettingLotsOfDataArtificialDataSynthesis.mp4 8.9 MB
  • 08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI.mp4 8.7 MB
  • 09.1-NeuralNetworksLearning-CostFunction.mp4 8.5 MB
  • 18.3-LargeScaleMachineLearning-MiniBatchGradientDescent.mp4 8.4 MB
  • 09.5-NeuralNetworksLearning-RandomInitialization.mp4 8.3 MB
  • 10.1-AdviceForApplyingMachineLearning-DecidingWhatToTryNext.mp4 8.0 MB
  • 04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling.mp4 8.0 MB
  • 02.8-V2-What'sNext.mp4 7.7 MB
  • 06.7-LogisticRegression-MultiClassClassificationOneVsAll.mp4 7.6 MB
  • 05.7-OctaveTutorial-WorkingOnAndSubmittingProgrammingExercises.mp4 7.6 MB
  • 18.1-LargeScaleMachineLearning-LearningWithLargeDatasets.mp4 7.5 MB
  • 15.2-DimensionalityReduction-MotivationIIVisualization.mp4 7.2 MB
  • 04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate.mp4 7.2 MB
  • 04.1-LinearRegressionWithMultipleVariables-MultipleFeatures.mp4 6.4 MB
  • 15.6-DimensionalityReduction-ReconstructionFromCompressedRepresentation.mp4 6.2 MB
  • 04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables.mp4 6.2 MB
  • 04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression.mp4 6.0 MB
  • 08.7-NeuralNetworksRepresentation-MultiClassClassification.mp4 5.7 MB
  • Self-notes/Lecture8.pdf 5.5 MB
  • 04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional).mp4 5.4 MB
  • Self-notes/Lecture1.pdf 4.9 MB
  • 20.1-Conclusion-SummaryAndThankYou.mp4 4.7 MB
  • 14.1-Clustering-UnsupervisedLearningIntroduction.mp4 4.3 MB
  • Self-notes/Lecture9.pdf 3.5 MB
  • Self-notes/Lecture15.pdf 3.5 MB
  • Self-notes/Lecture2.pdf 3.0 MB
  • Self-notes/Lecture12.pdf 2.4 MB
  • Self-notes/Lecture13.pdf 2.3 MB
  • Self-notes/Lecture3.pdf 1.9 MB
  • Self-notes/Lecture6.pdf 1.9 MB
  • Self-notes/Lecture4.pdf 1.8 MB
  • Self-notes/Lecture14.pdf 1.7 MB
  • Self-notes/Lecture10.pdf 1.6 MB
  • Self-notes/Lecture16.pdf 1.5 MB
  • Self-notes/Lecture7.pdf 1.5 MB
  • Self-notes/Lecture11.pdf 509.6 kB
  • Self-notes/octave_session.m 5.3 kB
  • Self-notes/12-10.txt 724 Bytes
  • Self-notes/08-10.txt 109 Bytes
  • Self-notes/22-11.txt 18 Bytes

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