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Machine Learning - Stanford

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File Name
Size
Self-notes/08-10.txt
109 B
Self-notes/12-10.txt
724 B
Self-notes/22-11.txt
18 B
Self-notes/Lecture1.pdf
4.7 MB
Self-notes/Lecture10.pdf
1.5 MB
Self-notes/Lecture11.pdf
498 kB
Self-notes/Lecture12.pdf
2.3 MB
Self-notes/Lecture13.pdf
2.2 MB
Self-notes/Lecture14.pdf
1.6 MB
Self-notes/Lecture15.pdf
3.3 MB
Self-notes/Lecture16.pdf
1.4 MB
Self-notes/Lecture2.pdf
2.9 MB
Self-notes/Lecture3.pdf
1.8 MB
Self-notes/Lecture4.pdf
1.7 MB
Self-notes/Lecture6.pdf
1.8 MB
Self-notes/Lecture7.pdf
1.4 MB
Self-notes/Lecture8.pdf
5.2 MB
Self-notes/Lecture9.pdf
3.4 MB
Self-notes/octave_session.m
5.2 kB
01.2-V2-Introduction-WhatIsMachineLearning.mp4
30 MB
01.3-V2-Introduction-SupervisedLearning.mp4
15 MB
01.4-V2-Introduction-UnsupervisedLearning.mp4
39 MB
02.1-V2-LinearRegressionWithOneVariable-ModelRepresentation.mp4
12 MB
02.2-V2-LinearRegressionWithOneVariable-CostFunction.mp4
13 MB
02.3-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionI.mp4
16 MB
02.4-V2-LinearRegressionWithOneVariable-CostFunctionIntuitionII.mp4
32 MB
02.5-V2-LinearRegressionWithOneVariable-GradientDescent.mp4
27 MB
02.6-V2-LinearRegressionWithOneVariable-GradientDescentIntuition.mp4
19 MB
02.7-V2-LinearRegressionWithOneVariable-GradientDescentForLinearRegression.mp4
26 MB
02.8-V2-What'sNext.mp4
7.3 MB
03.1-V2-LinearAlgebraReview(Optional)-MatricesAndVectors.mp4
12 MB
03.2-V2-LinearAlgebraReview(Optional)-AdditionAndScalarMultiplication.mp4
9.2 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
12 MB
03.6-V2-LinearAlgebraReview(Optional)-InverseAndTranspose.mp4
25 MB
04.1-LinearRegressionWithMultipleVariables-MultipleFeatures.mp4
6.1 MB
04.2-LinearRegressionWithMultipleVariables-GradientDescentForMultipleVariables.mp4
5.9 MB
04.3-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIFeatureScaling.mp4
7.6 MB
04.4-LinearRegressionWithMultipleVariables-GradientDescentInPracticeIILearningRate.mp4
6.9 MB
04.5-LinearRegressionWithMultipleVariables-FeaturesAndPolynomialRegression.mp4
5.7 MB
04.6-V2-LinearRegressionWithMultipleVariables-NormalEquation.mp4
13 MB
04.7-LinearRegressionWithMultipleVariables-NormalEquationNonInvertibility(Optional).mp4
5.2 MB
05.1-OctaveTutorial-BasicOperations.mp4
21 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
20 MB
05.6-OctaveTutorial-Vectorization.mp4
17 MB
05.7-OctaveTutorial-WorkingOnAndSubmittingProgrammingExercises.mp4
7.2 MB
06.1-LogisticRegression-Classification.mp4
8.7 MB
06.2-LogisticRegression-HypothesisRepresentation.mp4
8.8 MB
06.3-LogisticRegression-DecisionBoundary.mp4
18 MB
06.4-LogisticRegression-CostFunction.mp4
14 MB
06.5-LogisticRegression-SimplifiedCostFunctionAndGradientDescent.mp4
13 MB
06.6-LogisticRegression-AdvancedOptimization.mp4
22 MB
06.7-LogisticRegression-MultiClassClassificationOneVsAll.mp4
7.3 MB
07.1-Regularization-TheProblemOfOverfitting.mp4
12 MB
07.2-Regularization-CostFunction.mp4
12 MB
07.3-Regularization-RegularizedLinearRegression.mp4
13 MB
07.4-Regularization-RegularizedLogisticRegression.mp4
14 MB
08.1-NeuralNetworksRepresentation-NonLinearHypotheses.mp4
12 MB
08.2-NeuralNetworksRepresentation-NeuronsAndTheBrain.mp4
12 MB
08.3-NeuralNetworksRepresentation-ModelRepresentationI.mp4
14 MB
08.4-NeuralNetworksRepresentation-ModelRepresentationII.mp4
14 MB
08.5-NeuralNetworksRepresentation-ExamplesAndIntuitionsI.mp4
8.3 MB
08.6-NeuralNetworksRepresentation-ExamplesAndIntuitionsII.mp4
17 MB
08.7-NeuralNetworksRepresentation-MultiClassClassification.mp4
5.4 MB
09.1-NeuralNetworksLearning-CostFunction.mp4
8.1 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
15 MB
09.5-NeuralNetworksLearning-RandomInitialization.mp4
8.0 MB
09.7-NeuralNetworksLearning-PuttingItTogether.mp4
18 MB
09.8-NeuralNetworksLearning-AutonomousDrivingExample.mp4
21 MB
10.1-AdviceForApplyingMachineLearning-DecidingWhatToTryNext.mp4
7.6 MB
10.2-AdviceForApplyingMachineLearning-EvaluatingAHypothesis.mp4
9.5 MB
10.3-AdviceForApplyingMachineLearning-ModelSelectionAndTrainValidationTestSets.mp4
16 MB
10.4-AdviceForApplyingMachineLearning-DiagnosingBiasVsVariance.mp4
10 MB
10.5-AdviceForApplyingMachineLearning-RegularizationAndBiasVariance.mp4
14 MB
10.6-AdviceForApplyingMachineLearning-LearningCurves.mp4
14 MB
10.7-AdviceForApplyingMachineLearning-DecidingWhatToDoNextRevisited.mp4
8.9 MB
11.1-MachineLearningSystemDesign-PrioritizingWhatToWorkOn.mp4
12 MB
11.2-MachineLearningSystemDesign-ErrorAnalysis.mp4
17 MB
11.3-MachineLearningSystemDesign-ErrorMetricsForSkewedClasses.mp4
14 MB
11.4-MachineLearningSystemDesign-TradingOffPrecisionAndRecall.mp4
17 MB
11.5-MachineLearningSystemDesign-DataForMachineLearning.mp4
14 MB
12.1-SupportVectorMachines-OptimizationObjective.mp4
18 MB
12.2-SupportVectorMachines-LargeMarginIntuition.mp4
13 MB
12.3-SupportVectorMachines-MathematicsBehindLargeMarginClassificationOptional.mp4
23 MB
12.4-SupportVectorMachines-KernelsI.mp4
19 MB
12.5-SupportVectorMachines-KernelsII.mp4
18 MB
12.6-SupportVectorMachines-UsingAnSVM.mp4
26 MB
14.1-Clustering-UnsupervisedLearningIntroduction.mp4
4.1 MB
14.2-Clustering-KMeansAlgorithm.mp4
15 MB
14.3-Clustering-OptimizationObjective.mp4
8.8 MB
14.4-Clustering-RandomInitialization.mp4
9.3 MB
14.5-Clustering-ChoosingTheNumberOfClusters.mp4
10 MB
15.1-DimensionalityReduction-MotivationIDataCompression.mp4
18 MB
15.2-DimensionalityReduction-MotivationIIVisualization.mp4
6.9 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
5.9 MB
15.7-DimensionalityReduction-AdviceForApplyingPCA.mp4
16 MB
16.1-AnomalyDetection-ProblemMotivation-V1.mp4
8.8 MB
16.2-AnomalyDetection-GaussianDistribution.mp4
13 MB
16.3-AnomalyDetection-Algorithm.mp4
15 MB
16.4-AnomalyDetection-DevelopingAndEvaluatingAnAnomalyDetectionSystem.mp4
17 MB
16.5-AnomalyDetection-AnomalyDetectionVsSupervisedLearning-V1.mp4
11 MB
16.6-AnomalyDetection-ChoosingWhatFeaturesToUse.mp4
15 MB
16.7-AnomalyDetection-MultivariateGaussianDistribution-OPTIONAL.mp4
17 MB
16.8-AnomalyDetection-AnomalyDetectionUsingTheMultivariateGaussianDistribution-OPTIONAL.mp4
18 MB
17.1-RecommenderSystems-ProblemFormulation.mp4
14 MB
17.2-RecommenderSystems-ContentBasedRecommendations.mp4
19 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
7.1 MB
18.2-LargeScaleMachineLearning-StochasticGradientDescent.mp4
16 MB
18.3-LargeScaleMachineLearning-MiniBatchGradientDescent.mp4
8.0 MB
18.4-LargeScaleMachineLearning-StochasticGradientDescentConvergence.mp4
14 MB
18.5-LargeScaleMachineLearning-OnlineLearning.mp4
16 MB
18.6-LargeScaleMachineLearning-MapReduceAndDataParallelism.mp4
17 MB
19.1-ApplicationExamplePhotoOCR-ProblemDescriptionAndPipeline.mp4
8.5 MB
19.2-ApplicationExamplePhotoOCR-SlidingWindows.mp4
10 MB
19.3-ApplicationExamplePhotoOCR-GettingLotsOfDataArtificialDataSynthesis.mp4
8.5 MB
19.4-ApplicationExamplePhotoOCR-CeilingAnalysisWhatPartOfThePipelineToWorkOnNext.mp4
11 MB
20.1-Conclusion-SummaryAndThankYou.mp4
4.5 MB
Octave-3.2.4_i686-pc-mingw32_gcc-4.4.0_setup.exe
70 MB