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文档列表

027.SVMs in Practice/076. Using An SVM.mp4 33.5 MB
009.Octave Matlab Tutorial/028. Moving Data Around.mp4 31.0 MB
025.Large Margin Classification/073. Mathematics Behind Large Margin Classification.mp4 29.9 MB
019.Application of Neural Networks/058. Autonomous Driving.mp4 29.7 MB
011.Logistic Regression Model/038. Advanced Optimization.mp4 28.1 MB
040.Photo OCR/111. Getting Lots of Data and Artificial Data.mp4 26.5 MB
009.Octave Matlab Tutorial/027. Basic Operations.mp4 26.1 MB
030.Principal Component Analysis/085. Principal Component Analysis Algorithm.mp4 25.5 MB
009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.mp4 25.0 MB
007.Computing Parameters Analytically/024. Normal Equation.mp4 24.8 MB
018.Backpropagation in Practice/057. Putting It Together.mp4 24.7 MB
002.Introduction/005. Unsupervised Learning.mp4 24.5 MB
035.Predicting Movie Ratings/098. Content Based Recommendations.mp4 24.3 MB
026.Kernels/074. Kernels I.mp4 23.9 MB
026.Kernels/075. Kernels II.mp4 23.7 MB
034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.mp4 23.5 MB
009.Octave Matlab Tutorial/032. Vectorization.mp4 23.3 MB
017.Cost Function and Backpropagation/053. Backpropagation Intuition.mp4 23.3 MB
010.Classification and Representation/035. Decision Boundary.mp4 23.3 MB
040.Photo OCR/110. Sliding Windows.mp4 23.0 MB
040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp4 23.0 MB
025.Large Margin Classification/071. Optimization Objective.mp4 23.0 MB
034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.mp4 22.9 MB
029.Motivation/082. Motivation I Data Compression.mp4 22.5 MB
023.Handling Skewed Data/069. Trading Off Precision and Recall.mp4 22.3 MB
022.Building a Spam Classifier/067. Error Analysis.mp4 22.3 MB
039.Advanced Topics/108. Map Reduce and Data Parallelism.mp4 22.3 MB
038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.mp4 22.0 MB
016.Applications/049. Examples and Intuitions II.mp4 21.9 MB
033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.mp4 21.5 MB
039.Advanced Topics/107. Online Learning.mp4 21.5 MB
009.Octave Matlab Tutorial/030. Plotting Data.mp4 21.1 MB
009.Octave Matlab Tutorial/029. Computing on Data.mp4 20.8 MB
031.Applying PCA/088. Advice for Applying PCA.mp4 20.7 MB
033.Building an Anomaly Detection System/094. Choosing What Features to Use.mp4 20.0 MB
017.Cost Function and Backpropagation/052. Backpropagation Algorithm.mp4 20.0 MB
020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.mp4 20.0 MB
032.Density Estimation/091. Algorithm.mp4 19.9 MB
005.Linear Algebra Review/015. Matrix Vector Multiplication.mp4 19.8 MB
004.Parameter Learning/010. Gradient Descent.mp4 19.6 MB
015.Neural Networks/047. Model Representation II.mp4 19.3 MB
018.Backpropagation in Practice/055. Gradient Checking.mp4 19.2 MB
002.Introduction/002. Welcome.mp4 19.2 MB
038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.mp4 19.0 MB
015.Neural Networks/046. Model Representation I.mp4 18.9 MB
023.Handling Skewed Data/068. Error Metrics for Skewed Classes.mp4 18.8 MB
028.Clustering/078. K-Means Algorithm.mp4 18.5 MB
024.Using Large Data Sets/070. Data For Machine Learning.mp4 18.2 MB
005.Linear Algebra Review/018. Inverse and Transpose.mp4 17.8 MB
003.Model and Cost Function/009. Cost Function - Intuition II.mp4 17.8 MB
013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.mp4 17.6 MB
002.Introduction/004. Supervised Learning.mp4 17.5 MB
004.Parameter Learning/011. Gradient Descent Intuition.mp4 17.4 MB
004.Parameter Learning/012. Gradient Descent For Linear Regression.mp4 17.2 MB
035.Predicting Movie Ratings/097. Problem Formulation.mp4 17.2 MB
021.Bias vs. Variance/064. Learning Curves.mp4 17.2 MB
021.Bias vs. Variance/063. Regularization and Bias Variance.mp4 17.2 MB
005.Linear Algebra Review/016. Matrix Matrix Multiplication.mp4 17.1 MB
011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.mp4 17.0 MB
011.Logistic Regression Model/036. Cost Function.mp4 16.6 MB
031.Applying PCA/087. Choosing the Number of Principal Components.mp4 16.4 MB
013.Solving the Problem of Overfitting/042. Regularized Linear Regression.mp4 16.4 MB
003.Model and Cost Function/008. Cost Function - Intuition I.mp4 16.3 MB
036.Collaborative Filtering/099. Collaborative Filtering.mp4 16.3 MB
013.Solving the Problem of Overfitting/041. Cost Function.mp4 16.3 MB
025.Large Margin Classification/072. Large Margin Intuition.mp4 15.9 MB
032.Density Estimation/090. Gaussian Distribution.mp4 15.9 MB
022.Building a Spam Classifier/066. Prioritizing What to Work On.mp4 15.8 MB
013.Solving the Problem of Overfitting/040. The Problem of Overfitting.mp4 15.7 MB
014.Motivations/044. Non-linear Hypotheses.mp4 15.5 MB
036.Collaborative Filtering/100. Collaborative Filtering Algorithm.mp4 15.4 MB
014.Motivations/045. Neurons and the Brain.mp4 15.3 MB
030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.mp4 14.7 MB
033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.mp4 13.8 MB
006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.mp4 13.6 MB
018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.mp4 13.5 MB
037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.mp4 13.5 MB
037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.mp4 13.4 MB
006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.mp4 13.2 MB
028.Clustering/081. Choosing the Number of Clusters.mp4 12.8 MB
021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.mp4 12.8 MB
005.Linear Algebra Review/017. Matrix Multiplication Properties.mp4 12.7 MB
005.Linear Algebra Review/013. Matrices and Vectors.mp4 12.5 MB
006.Multivariate Linear Regression/019. Multiple Features.mp4 12.1 MB
006.Multivariate Linear Regression/023. Features and Polynomial Regression.mp4 12.1 MB
003.Model and Cost Function/007. Cost Function.mp4 12.1 MB
021.Bias vs. Variance/065. Deciding What to Do Next Revisited.mp4 12.0 MB
003.Model and Cost Function/006. Model Representation.mp4 12.0 MB
002.Introduction/003. What is Machine Learning.mp4 12.0 MB
010.Classification and Representation/033. Classification.mp4 11.9 MB
010.Classification and Representation/034. Hypothesis Representation.mp4 11.7 MB
028.Clustering/080. Random Initialization.mp4 11.7 MB
020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.mp4 11.6 MB
028.Clustering/079. Optimization Objective.mp4 11.4 MB
032.Density Estimation/089. Problem Motivation.mp4 11.1 MB
040.Photo OCR/109. Problem Description and Pipeline.mp4 10.9 MB
017.Cost Function and Backpropagation/051. Cost Function.mp4 10.7 MB
016.Applications/048. Examples and Intuitions I.mp4 10.6 MB
018.Backpropagation in Practice/056. Random Initialization.mp4 10.3 MB
038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.mp4 10.2 MB
020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.mp4 9.8 MB
005.Linear Algebra Review/014. Addition and Scalar Multiplication.mp4 9.7 MB
001.Welcome/001. Welcome to Machine Learning!.mp4 9.6 MB
041.Conclusion/113. Summary and Thank You.mp4 9.5 MB
012.Multiclass Classification/039. Multiclass Classification One-vs-all.mp4 9.5 MB
008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.mp4 9.4 MB
007.Computing Parameters Analytically/025. Normal Equation Noninvertibility.mp4 9.2 MB
038.Gradient Descent with Large Datasets/103. Learning With Large Datasets.mp4 9.0 MB
029.Motivation/083. Motivation II Visualization.mp4 8.7 MB
006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.mp4 8.0 MB
031.Applying PCA/086. Reconstruction from Compressed Representation.mp4 7.5 MB
016.Applications/050. Multiclass Classification.mp4 7.3 MB
028.Clustering/077. Unsupervised Learning Introduction.mp4 5.4 MB
027.SVMs in Practice/076. Using An SVM.srt 42.1 kB
025.Large Margin Classification/073. Mathematics Behind Large Margin Classification.srt 34.6 kB
040.Photo OCR/111. Getting Lots of Data and Artificial Data.srt 34.0 kB
040.Photo OCR/110. Sliding Windows.srt 30.4 kB
007.Computing Parameters Analytically/024. Normal Equation.srt 30.2 kB
026.Kernels/075. Kernels II.srt 29.6 kB
002.Introduction/005. Unsupervised Learning.srt 28.1 kB
026.Kernels/074. Kernels I.srt 28.0 kB
039.Advanced Topics/108. Map Reduce and Data Parallelism.srt 27.9 kB
009.Octave Matlab Tutorial/028. Moving Data Around.srt 27.6 kB
030.Principal Component Analysis/085. Principal Component Analysis Algorithm.srt 27.6 kB
011.Logistic Regression Model/038. Advanced Optimization.srt 26.9 kB
018.Backpropagation in Practice/057. Putting It Together.srt 26.8 kB
039.Advanced Topics/107. Online Learning.srt 26.7 kB
034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.srt 26.5 kB
033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.srt 26.4 kB
034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.srt 25.4 kB
031.Applying PCA/088. Advice for Applying PCA.srt 25.4 kB
028.Clustering/078. K-Means Algorithm.srt 25.3 kB
009.Octave Matlab Tutorial/027. Basic Operations.srt 24.5 kB
033.Building an Anomaly Detection System/094. Choosing What Features to Use.srt 24.3 kB
021.Bias vs. Variance/064. Learning Curves.srt 23.9 kB
005.Linear Algebra Review/015. Matrix Vector Multiplication.srt 23.4 kB
032.Density Estimation/091. Algorithm.srt 22.7 kB
009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.srt 22.6 kB
024.Using Large Data Sets/070. Data For Machine Learning.srt 22.4 kB
040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.srt 22.3 kB
017.Cost Function and Backpropagation/052. Backpropagation Algorithm.srt 22.0 kB
015.Neural Networks/047. Model Representation II.srt 21.6 kB
023.Handling Skewed Data/068. Error Metrics for Skewed Classes.srt 21.3 kB
025.Large Margin Classification/072. Large Margin Intuition.srt 20.6 kB
031.Applying PCA/087. Choosing the Number of Principal Components.srt 20.4 kB
005.Linear Algebra Review/018. Inverse and Transpose.srt 20.3 kB
025.Large Margin Classification/071. Optimization Objective.srt 20.3 kB
023.Handling Skewed Data/069. Trading Off Precision and Recall.srt 20.1 kB
035.Predicting Movie Ratings/098. Content Based Recommendations.srt 20.0 kB
022.Building a Spam Classifier/067. Error Analysis.srt 19.8 kB
036.Collaborative Filtering/099. Collaborative Filtering.srt 19.5 kB
029.Motivation/082. Motivation I Data Compression.srt 19.4 kB
002.Introduction/004. Supervised Learning.srt 19.3 kB
013.Solving the Problem of Overfitting/041. Cost Function.srt 19.1 kB
022.Building a Spam Classifier/066. Prioritizing What to Work On.srt 19.0 kB
013.Solving the Problem of Overfitting/040. The Problem of Overfitting.srt 18.6 kB
014.Motivations/044. Non-linear Hypotheses.srt 18.4 kB
010.Classification and Representation/035. Decision Boundary.srt 18.3 kB
017.Cost Function and Backpropagation/053. Backpropagation Intuition.srt 18.1 kB
038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.srt 18.0 kB
009.Octave Matlab Tutorial/032. Vectorization.srt 17.7 kB
018.Backpropagation in Practice/055. Gradient Checking.srt 17.4 kB
020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.srt 17.3 kB
028.Clustering/081. Choosing the Number of Clusters.srt 17.3 kB
009.Octave Matlab Tutorial/029. Computing on Data.srt 17.1 kB
009.Octave Matlab Tutorial/030. Plotting Data.srt 16.7 kB
004.Parameter Learning/010. Gradient Descent.srt 16.7 kB
013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.srt 16.6 kB
006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.srt 16.4 kB
004.Parameter Learning/011. Gradient Descent Intuition.srt 16.3 kB
035.Predicting Movie Ratings/097. Problem Formulation.srt 16.2 kB
038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.srt 16.0 kB
037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.srt 16.0 kB
036.Collaborative Filtering/100. Collaborative Filtering Algorithm.srt 15.9 kB
014.Motivations/045. Neurons and the Brain.srt 15.8 kB
037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.srt 15.7 kB
028.Clustering/080. Random Initialization.srt 15.7 kB
032.Density Estimation/089. Problem Motivation.srt 15.5 kB
006.Multivariate Linear Regression/023. Features and Polynomial Regression.srt 15.3 kB
005.Linear Algebra Review/013. Matrices and Vectors.srt 15.3 kB
021.Bias vs. Variance/063. Regularization and Bias Variance.srt 15.3 kB
032.Density Estimation/090. Gaussian Distribution.srt 14.9 kB
015.Neural Networks/046. Model Representation I.srt 14.8 kB
013.Solving the Problem of Overfitting/042. Regularized Linear Regression.srt 14.5 kB
018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.srt 14.4 kB
011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.srt 14.3 kB
040.Photo OCR/109. Problem Description and Pipeline.srt 14.2 kB
006.Multivariate Linear Regression/019. Multiple Features.srt 14.0 kB
005.Linear Algebra Review/016. Matrix Matrix Multiplication.srt 14.0 kB
004.Parameter Learning/012. Gradient Descent For Linear Regression.srt 13.7 kB
011.Logistic Regression Model/036. Cost Function.srt 13.7 kB
021.Bias vs. Variance/065. Deciding What to Do Next Revisited.srt 13.6 kB
030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.srt 13.4 kB
006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.srt 12.8 kB
003.Model and Cost Function/008. Cost Function - Intuition I.srt 12.0 kB
020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.srt 12.0 kB
005.Linear Algebra Review/017. Matrix Multiplication Properties.srt 11.8 kB
016.Applications/049. Examples and Intuitions II.srt 11.7 kB
010.Classification and Representation/033. Classification.srt 11.7 kB
005.Linear Algebra Review/014. Addition and Scalar Multiplication.srt 11.5 kB
033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.srt 11.5 kB
021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.srt 11.5 kB
002.Introduction/003. What is Machine Learning.srt 11.3 kB
020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.srt 11.2 kB
003.Model and Cost Function/009. Cost Function - Intuition II.srt 11.0 kB
018.Backpropagation in Practice/056. Random Initialization.srt 10.6 kB
003.Model and Cost Function/007. Cost Function.srt 10.4 kB
010.Classification and Representation/034. Hypothesis Representation.srt 9.8 kB
029.Motivation/083. Motivation II Visualization.srt 9.8 kB
003.Model and Cost Function/006. Model Representation.srt 9.8 kB
002.Introduction/002. Welcome.srt 9.7 kB
028.Clustering/079. Optimization Objective.srt 9.5 kB
012.Multiclass Classification/039. Multiclass Classification One-vs-all.srt 9.5 kB
017.Cost Function and Backpropagation/051. Cost Function.srt 9.1 kB
007.Computing Parameters Analytically/025. Normal Equation Noninvertibility.srt 8.9 kB
016.Applications/048. Examples and Intuitions I.srt 8.7 kB
041.Conclusion/113. Summary and Thank You.srt 7.9 kB
038.Gradient Descent with Large Datasets/103. Learning With Large Datasets.srt 7.8 kB
038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.srt 7.7 kB
016.Applications/050. Multiclass Classification.srt 7.2 kB
019.Application of Neural Networks/058. Autonomous Driving.srt 7.0 kB
006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.srt 6.5 kB
031.Applying PCA/086. Reconstruction from Compressed Representation.srt 5.2 kB
028.Clustering/077. Unsupervised Learning Introduction.srt 5.1 kB
008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.srt 4.4 kB
001.Welcome/001. Welcome to Machine Learning!.srt 2.4 kB
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