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[Coursera] Machine Learning (Stanford University) (ml)

by: Stanford University

Publisher: Academic Torrents

Contributor: Academic Torrents

Academictorrents_collection: video-lectures

Addeddate: 2018-08-12 13:36:42

External-identifier: urn:academictorrents:e8b1f9c5bf555fe58bc73addb83457dd6da69630

Identifier: academictorrents_e8b1f9c5bf555fe58bc73addb83457dd6da69630

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Ppi: 600

Scanner: Internet Archive Python library 1.8.1

Source: http://academictorrents.com/details/e8b1f9c5bf555fe58bc73addb83457dd6da69630
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18 files

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BACK

127.7K

01_Welcome_7_min_abbyy.gz

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01_Model_Representation_8_min_abbyy.gz

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01_Matrices_and_Vectors_9_min_abbyy.gz

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01_Basic_Operations_14_min_abbyy.gz

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01_Classification_8_min_abbyy.gz

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01_The_Problem_of_Overfitting_10_min_abbyy.gz

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01_Non-linear_Hypotheses_10_min_abbyy.gz

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01_Cost_Function_7_min_abbyy.gz

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02_What_is_Machine_Learning_7_min.mp4

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9.6M

01_Learning_With_Large_Datasets_6_min_jp2.zip

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01_Problem_Description_and_Pipeline_7_min_jp2.zip

113 files

SUBRIP

BACK

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01_Welcome_7_min.srt

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02_What_is_Machine_Learning_7_min.srt

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03_Supervised_Learning_12_min.srt

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04_Unsupervised_Learning_14_min.srt

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01_Model_Representation_8_min.srt

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02_Cost_Function_8_min.srt

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03_Cost_Function_-_Intuition_I_11_min.srt

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04_Cost_Function_-_Intuition_II_9_min.srt

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05_Gradient_Descent_11_min.srt

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06_Gradient_Descent_Intuition_12_min.srt

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07_Gradient_Descent_For_Linear_Regression_10_min.srt

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08_Whats_Next_6_min.srt

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01_Matrices_and_Vectors_9_min.srt

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02_Addition_and_Scalar_Multiplication_7_min.srt

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03_Matrix_Vector_Multiplication_14_min.srt

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04_Matrix_Matrix_Multiplication_11_min.srt

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05_Matrix_Multiplication_Properties_9_min.srt

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06_Inverse_and_Transpose_11_min.srt

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01_Multiple_Features_8_min.srt

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02_Gradient_Descent_for_Multiple_Variables_5_min.srt

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03_Gradient_Descent_in_Practice_I_-_Feature_Scaling_9_min.srt

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04_Gradient_Descent_in_Practice_II_-_Learning_Rate_9_min.srt

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05_Features_and_Polynomial_Regression_8_min.srt

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06_Normal_Equation_16_min.srt

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07_Normal_Equation_Noninvertibility_Optional_6_min.srt

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01_Basic_Operations_14_min.srt

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02_Moving_Data_Around_16_min.srt

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03_Computing_on_Data_13_min.srt

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04_Plotting_Data_10_min.srt

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05_Control_Statements-_for_while_if_statements_13_min.srt

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06_Vectorization_14_min.srt

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07_Working_on_and_Submitting_Programming_Exercises_4_min.srt

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01_Classification_8_min.srt

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02_Hypothesis_Representation_7_min.srt

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03_Decision_Boundary_15_min.srt

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04_Cost_Function_11_min.srt

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05_Simplified_Cost_Function_and_Gradient_Descent_10_min.srt

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06_Advanced_Optimization_14_min.srt

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07_Multiclass_Classification-_One-vs-all_6_min.srt

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01_The_Problem_of_Overfitting_10_min.srt

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02_Cost_Function_10_min.srt

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03_Regularized_Linear_Regression_11_min.srt

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04_Regularized_Logistic_Regression_9_min.srt

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01_Non-linear_Hypotheses_10_min.srt

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02_Neurons_and_the_Brain_8_min.srt

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03_Model_Representation_I_12_min.srt

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04_Model_Representation_II_12_min.srt

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05_Examples_and_Intuitions_I_7_min.srt

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06_Examples_and_Intuitions_II_10_min.srt

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07_Multiclass_Classification_4_min.srt

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01_Cost_Function_7_min.srt

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02_Backpropagation_Algorithm_12_min.srt

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03_Backpropagation_Intuition_13_min.srt

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04_Implementation_Note-_Unrolling_Parameters_8_min.srt

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05_Gradient_Checking_12_min.srt

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06_Random_Initialization_7_min.srt

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07_Putting_It_Together_14_min.srt

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08_Autonomous_Driving_7_min.srt

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01_Deciding_What_to_Try_Next_6_min.srt

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02_Evaluating_a_Hypothesis_8_min.srt

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03_Model_Selection_and_Train-Validation-Test_Sets_12_min.srt

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04_Diagnosing_Bias_vs._Variance_8_min.srt

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05_Regularization_and_Bias-Variance_11_min.srt

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06_Learning_Curves_12_min.srt

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07_Deciding_What_to_Do_Next_Revisited_7_min.srt

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01_Prioritizing_What_to_Work_On_10_min.srt

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02_Error_Analysis_13_min.srt

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03_Error_Metrics_for_Skewed_Classes_12_min.srt

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04_Trading_Off_Precision_and_Recall_14_min.srt

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05_Data_For_Machine_Learning_11_min.srt

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01_Optimization_Objective_15_min.srt

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02_Large_Margin_Intuition_11_min.srt

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03_Mathematics_Behind_Large_Margin_Classification_Optional_20_min.srt

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04_Kernels_I_16_min.srt

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05_Kernels_II_16_min.srt

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06_Using_An_SVM_21_min.srt

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01_Unsupervised_Learning-_Introduction_3_min.srt

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02_K-Means_Algorithm_13_min.srt

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03_Optimization_Objective_7_min.srt

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04_Random_Initialization_8_min.srt

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05_Choosing_the_Number_of_Clusters_8_min.srt

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01_Motivation_I-_Data_Compression_10_min.srt

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02_Motivation_II-_Visualization_6_min.srt

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03_Principal_Component_Analysis_Problem_Formulation_9_min.srt

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04_Principal_Component_Analysis_Algorithm_15_min.srt

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05_Choosing_the_Number_of_Principal_Components_11_min.srt

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06_Reconstruction_from_Compressed_Representation_4_min.srt

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07_Advice_for_Applying_PCA_13_min.srt

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01_Problem_Motivation_8_min.srt

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02_Gaussian_Distribution_10_min.srt

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03_Algorithm_12_min.srt

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04_Developing_and_Evaluating_an_Anomaly_Detection_System_13_min.srt

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05_Anomaly_Detection_vs._Supervised_Learning_8_min.srt

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06_Choosing_What_Features_to_Use_12_min.srt

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07_Multivariate_Gaussian_Distribution_Optional_14_min.srt

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08_Anomaly_Detection_using_the_Multivariate_Gaussian_Distribution_Optional_14_min.srt

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01_Problem_Formulation_8_min.srt

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02_Content_Based_Recommendations_15_min.srt

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03_Collaborative_Filtering_10_min.srt

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04_Collaborative_Filtering_Algorithm_9_min.srt

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05_Vectorization-_Low_Rank_Matrix_Factorization_8_min.srt

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06_Implementational_Detail-_Mean_Normalization_9_min.srt

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01_Learning_With_Large_Datasets_6_min.srt

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02_Stochastic_Gradient_Descent_13_min.srt

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03_Mini-Batch_Gradient_Descent_6_min.srt

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04_Stochastic_Gradient_Descent_Convergence_12_min.srt

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05_Online_Learning_13_min.srt

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06_Map_Reduce_and_Data_Parallelism_14_min.srt

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01_Problem_Description_and_Pipeline_7_min.srt

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02_Sliding_Windows_15_min.srt

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03_Getting_Lots_of_Data_and_Artificial_Data_16_min.srt

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04_Ceiling_Analysis-_What_Part_of_the_Pipeline_to_Work_on_Next_14_min.srt

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01_Summary_and_Thank_You_5_min.srt

113 files

TEXT

BACK

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01_Welcome_7_min.txt

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02_What_is_Machine_Learning_7_min.txt

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03_Supervised_Learning_12_min.txt

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04_Unsupervised_Learning_14_min.txt

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01_Model_Representation_8_min.txt

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02_Cost_Function_8_min.txt

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03_Cost_Function_-_Intuition_I_11_min.txt

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04_Cost_Function_-_Intuition_II_9_min.txt

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05_Gradient_Descent_11_min.txt

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06_Gradient_Descent_Intuition_12_min.txt

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07_Gradient_Descent_For_Linear_Regression_10_min.txt

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08_Whats_Next_6_min.txt

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01_Matrices_and_Vectors_9_min.txt

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02_Addition_and_Scalar_Multiplication_7_min.txt

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03_Matrix_Vector_Multiplication_14_min.txt

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04_Matrix_Matrix_Multiplication_11_min.txt

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05_Matrix_Multiplication_Properties_9_min.txt

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06_Inverse_and_Transpose_11_min.txt

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01_Multiple_Features_8_min.txt

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02_Gradient_Descent_for_Multiple_Variables_5_min.txt

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03_Gradient_Descent_in_Practice_I_-_Feature_Scaling_9_min.txt

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04_Gradient_Descent_in_Practice_II_-_Learning_Rate_9_min.txt

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05_Features_and_Polynomial_Regression_8_min.txt

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06_Normal_Equation_16_min.txt

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07_Normal_Equation_Noninvertibility_Optional_6_min.txt

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01_Basic_Operations_14_min.txt

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02_Moving_Data_Around_16_min.txt

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03_Computing_on_Data_13_min.txt

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04_Plotting_Data_10_min.txt

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05_Control_Statements-_for_while_if_statements_13_min.txt

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06_Vectorization_14_min.txt

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07_Working_on_and_Submitting_Programming_Exercises_4_min.txt

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01_Classification_8_min.txt

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02_Hypothesis_Representation_7_min.txt

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03_Decision_Boundary_15_min.txt

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04_Cost_Function_11_min.txt

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05_Simplified_Cost_Function_and_Gradient_Descent_10_min.txt

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06_Advanced_Optimization_14_min.txt

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07_Multiclass_Classification-_One-vs-all_6_min.txt

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01_The_Problem_of_Overfitting_10_min.txt

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02_Cost_Function_10_min.txt

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03_Regularized_Linear_Regression_11_min.txt

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04_Regularized_Logistic_Regression_9_min.txt

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01_Non-linear_Hypotheses_10_min.txt

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02_Neurons_and_the_Brain_8_min.txt

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03_Model_Representation_I_12_min.txt

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04_Model_Representation_II_12_min.txt

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05_Examples_and_Intuitions_I_7_min.txt

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06_Examples_and_Intuitions_II_10_min.txt

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07_Multiclass_Classification_4_min.txt

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01_Cost_Function_7_min.txt

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02_Backpropagation_Algorithm_12_min.txt

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03_Backpropagation_Intuition_13_min.txt

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04_Implementation_Note-_Unrolling_Parameters_8_min.txt

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05_Gradient_Checking_12_min.txt

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06_Random_Initialization_7_min.txt

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07_Putting_It_Together_14_min.txt

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08_Autonomous_Driving_7_min.txt

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01_Deciding_What_to_Try_Next_6_min.txt

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02_Evaluating_a_Hypothesis_8_min.txt

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03_Model_Selection_and_Train-Validation-Test_Sets_12_min.txt

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04_Diagnosing_Bias_vs._Variance_8_min.txt

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05_Regularization_and_Bias-Variance_11_min.txt

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06_Learning_Curves_12_min.txt

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07_Deciding_What_to_Do_Next_Revisited_7_min.txt

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01_Prioritizing_What_to_Work_On_10_min.txt

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02_Error_Analysis_13_min.txt

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03_Error_Metrics_for_Skewed_Classes_12_min.txt

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04_Trading_Off_Precision_and_Recall_14_min.txt

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05_Data_For_Machine_Learning_11_min.txt

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01_Optimization_Objective_15_min.txt

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02_Large_Margin_Intuition_11_min.txt

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03_Mathematics_Behind_Large_Margin_Classification_Optional_20_min.txt

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04_Kernels_I_16_min.txt

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05_Kernels_II_16_min.txt

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06_Using_An_SVM_21_min.txt

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01_Unsupervised_Learning-_Introduction_3_min.txt

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02_K-Means_Algorithm_13_min.txt

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03_Optimization_Objective_7_min.txt

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04_Random_Initialization_8_min.txt

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05_Choosing_the_Number_of_Clusters_8_min.txt

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01_Motivation_I-_Data_Compression_10_min.txt

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02_Motivation_II-_Visualization_6_min.txt

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03_Principal_Component_Analysis_Problem_Formulation_9_min.txt

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04_Principal_Component_Analysis_Algorithm_15_min.txt

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05_Choosing_the_Number_of_Principal_Components_11_min.txt

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06_Reconstruction_from_Compressed_Representation_4_min.txt

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07_Advice_for_Applying_PCA_13_min.txt

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01_Problem_Motivation_8_min.txt

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02_Gaussian_Distribution_10_min.txt

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03_Algorithm_12_min.txt

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04_Developing_and_Evaluating_an_Anomaly_Detection_System_13_min.txt

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05_Anomaly_Detection_vs._Supervised_Learning_8_min.txt

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06_Choosing_What_Features_to_Use_12_min.txt

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07_Multivariate_Gaussian_Distribution_Optional_14_min.txt

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08_Anomaly_Detection_using_the_Multivariate_Gaussian_Distribution_Optional_14_min.txt

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01_Problem_Formulation_8_min.txt

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02_Content_Based_Recommendations_15_min.txt

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03_Collaborative_Filtering_10_min.txt

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04_Collaborative_Filtering_Algorithm_9_min.txt

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05_Vectorization-_Low_Rank_Matrix_Factorization_8_min.txt

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06_Implementational_Detail-_Mean_Normalization_9_min.txt

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01_Learning_With_Large_Datasets_6_min.txt

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02_Stochastic_Gradient_Descent_13_min.txt

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03_Mini-Batch_Gradient_Descent_6_min.txt

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04_Stochastic_Gradient_Descent_Convergence_12_min.txt

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05_Online_Learning_13_min.txt

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06_Map_Reduce_and_Data_Parallelism_14_min.txt

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01_Problem_Description_and_Pipeline_7_min.txt

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02_Sliding_Windows_15_min.txt

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03_Getting_Lots_of_Data_and_Artificial_Data_16_min.txt

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04_Ceiling_Analysis-_What_Part_of_the_Pipeline_to_Work_on_Next_14_min.txt

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01_Summary_and_Thank_You_5_min.txt

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Uploaded by arkiver2 on August 12, 2018

[Coursera] Machine Learning (Stanford University) (ml)

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