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[CourseClub.NET] Coursera - Machine Learning

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视频 2019-3-6 04:10 2024-11-4 22:10 110 1.81 GB 113
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CourseClub NET Coursera - Machine Learning

文件列表
  1. 001.Welcome/001. Welcome to Machine Learning!.mp49.13MB
  2. 002.Introduction/002. Welcome.mp418.28MB
  3. 002.Introduction/003. What is Machine Learning.mp411.41MB
  4. 002.Introduction/004. Supervised Learning.mp416.68MB
  5. 002.Introduction/005. Unsupervised Learning.mp423.33MB
  6. 003.Model and Cost Function/006. Model Representation.mp411.42MB
  7. 003.Model and Cost Function/007. Cost Function.mp411.51MB
  8. 003.Model and Cost Function/008. Cost Function - Intuition I.mp415.53MB
  9. 003.Model and Cost Function/009. Cost Function - Intuition II.mp416.99MB
  10. 004.Parameter Learning/010. Gradient Descent.mp418.72MB
  11. 004.Parameter Learning/011. Gradient Descent Intuition.mp416.61MB
  12. 004.Parameter Learning/012. Gradient Descent For Linear Regression.mp416.43MB
  13. 005.Linear Algebra Review/013. Matrices and Vectors.mp411.94MB
  14. 005.Linear Algebra Review/014. Addition and Scalar Multiplication.mp49.27MB
  15. 005.Linear Algebra Review/015. Matrix Vector Multiplication.mp418.93MB
  16. 005.Linear Algebra Review/016. Matrix Matrix Multiplication.mp416.29MB
  17. 005.Linear Algebra Review/017. Matrix Multiplication Properties.mp412.15MB
  18. 005.Linear Algebra Review/018. Inverse and Transpose.mp417.01MB
  19. 006.Multivariate Linear Regression/019. Multiple Features.mp411.58MB
  20. 006.Multivariate Linear Regression/020. Gradient Descent for Multiple Variables.mp47.62MB
  21. 006.Multivariate Linear Regression/021. Gradient Descent in Practice I - Feature Scaling.mp412.94MB
  22. 006.Multivariate Linear Regression/022. Gradient Descent in Practice II - Learning Rate.mp412.56MB
  23. 006.Multivariate Linear Regression/023. Features and Polynomial Regression.mp411.54MB
  24. 007.Computing Parameters Analytically/024. Normal Equation.mp423.63MB
  25. 007.Computing Parameters Analytically/025. Normal Equation Noninvertibility.mp48.8MB
  26. 008.Submitting Programming Assignments/026. Working on and Submitting Programming Assignments.mp48.96MB
  27. 009.Octave Matlab Tutorial/027. Basic Operations.mp424.9MB
  28. 009.Octave Matlab Tutorial/028. Moving Data Around.mp429.53MB
  29. 009.Octave Matlab Tutorial/029. Computing on Data.mp419.81MB
  30. 009.Octave Matlab Tutorial/030. Plotting Data.mp420.08MB
  31. 009.Octave Matlab Tutorial/031. Control Statements for, while, if statement.mp423.88MB
  32. 009.Octave Matlab Tutorial/032. Vectorization.mp422.27MB
  33. 010.Classification and Representation/033. Classification.mp411.32MB
  34. 010.Classification and Representation/034. Hypothesis Representation.mp411.17MB
  35. 010.Classification and Representation/035. Decision Boundary.mp422.19MB
  36. 011.Logistic Regression Model/036. Cost Function.mp415.83MB
  37. 011.Logistic Regression Model/037. Simplified Cost Function and Gradient Descent.mp416.26MB
  38. 011.Logistic Regression Model/038. Advanced Optimization.mp426.77MB
  39. 012.Multiclass Classification/039. Multiclass Classification One-vs-all.mp49.07MB
  40. 013.Solving the Problem of Overfitting/040. The Problem of Overfitting.mp414.93MB
  41. 013.Solving the Problem of Overfitting/041. Cost Function.mp415.51MB
  42. 013.Solving the Problem of Overfitting/042. Regularized Linear Regression.mp415.63MB
  43. 013.Solving the Problem of Overfitting/043. Regularized Logistic Regression.mp416.77MB
  44. 014.Motivations/044. Non-linear Hypotheses.mp414.74MB
  45. 014.Motivations/045. Neurons and the Brain.mp414.57MB
  46. 015.Neural Networks/046. Model Representation I.mp418MB
  47. 015.Neural Networks/047. Model Representation II.mp418.4MB
  48. 016.Applications/048. Examples and Intuitions I.mp410.07MB
  49. 016.Applications/049. Examples and Intuitions II.mp420.93MB
  50. 016.Applications/050. Multiclass Classification.mp47MB
  51. 017.Cost Function and Backpropagation/051. Cost Function.mp410.25MB
  52. 017.Cost Function and Backpropagation/052. Backpropagation Algorithm.mp419.07MB
  53. 017.Cost Function and Backpropagation/053. Backpropagation Intuition.mp422.23MB
  54. 018.Backpropagation in Practice/054. Implementation Note Unrolling Parameters.mp412.92MB
  55. 018.Backpropagation in Practice/055. Gradient Checking.mp418.35MB
  56. 018.Backpropagation in Practice/056. Random Initialization.mp49.81MB
  57. 018.Backpropagation in Practice/057. Putting It Together.mp423.55MB
  58. 019.Application of Neural Networks/058. Autonomous Driving.mp428.3MB
  59. 020.Evaluating a Learning Algorithm/059. Deciding What to Try Next.mp49.35MB
  60. 020.Evaluating a Learning Algorithm/060. Evaluating a Hypothesis.mp411.05MB
  61. 020.Evaluating a Learning Algorithm/061. Model Selection and Train Validation Test Sets.mp419.04MB
  62. 021.Bias vs. Variance/062. Diagnosing Bias vs. Variance.mp412.18MB
  63. 021.Bias vs. Variance/063. Regularization and Bias Variance.mp416.39MB
  64. 021.Bias vs. Variance/064. Learning Curves.mp416.39MB
  65. 021.Bias vs. Variance/065. Deciding What to Do Next Revisited.mp411.43MB
  66. 022.Building a Spam Classifier/066. Prioritizing What to Work On.mp415.06MB
  67. 022.Building a Spam Classifier/067. Error Analysis.mp421.27MB
  68. 023.Handling Skewed Data/068. Error Metrics for Skewed Classes.mp417.95MB
  69. 023.Handling Skewed Data/069. Trading Off Precision and Recall.mp421.3MB
  70. 024.Using Large Data Sets/070. Data For Machine Learning.mp417.31MB
  71. 025.Large Margin Classification/071. Optimization Objective.mp421.89MB
  72. 025.Large Margin Classification/072. Large Margin Intuition.mp415.21MB
  73. 025.Large Margin Classification/073. Mathematics Behind Large Margin Classification.mp428.48MB
  74. 026.Kernels/074. Kernels I.mp422.81MB
  75. 026.Kernels/075. Kernels II.mp422.63MB
  76. 027.SVMs in Practice/076. Using An SVM.mp431.99MB
  77. 028.Clustering/077. Unsupervised Learning Introduction.mp45.16MB
  78. 028.Clustering/078. K-Means Algorithm.mp417.67MB
  79. 028.Clustering/079. Optimization Objective.mp410.92MB
  80. 028.Clustering/080. Random Initialization.mp411.15MB
  81. 028.Clustering/081. Choosing the Number of Clusters.mp412.22MB
  82. 029.Motivation/082. Motivation I Data Compression.mp421.45MB
  83. 029.Motivation/083. Motivation II Visualization.mp48.3MB
  84. 030.Principal Component Analysis/084. Principal Component Analysis Problem Formulation.mp413.98MB
  85. 030.Principal Component Analysis/085. Principal Component Analysis Algorithm.mp424.29MB
  86. 031.Applying PCA/086. Reconstruction from Compressed Representation.mp47.16MB
  87. 031.Applying PCA/087. Choosing the Number of Principal Components.mp415.64MB
  88. 031.Applying PCA/088. Advice for Applying PCA.mp419.74MB
  89. 032.Density Estimation/089. Problem Motivation.mp410.56MB
  90. 032.Density Estimation/090. Gaussian Distribution.mp415.19MB
  91. 032.Density Estimation/091. Algorithm.mp418.94MB
  92. 033.Building an Anomaly Detection System/092. Developing and Evaluating an Anomaly Detection System.mp420.53MB
  93. 033.Building an Anomaly Detection System/093. Anomaly Detection vs. Supervised Learning.mp413.15MB
  94. 033.Building an Anomaly Detection System/094. Choosing What Features to Use.mp419.09MB
  95. 034.Multivariate Gaussian Distribution (Optional)/095. Multivariate Gaussian Distribution.mp421.86MB
  96. 034.Multivariate Gaussian Distribution (Optional)/096. Anomaly Detection using the Multivariate Gaussian Distribution.mp422.42MB
  97. 035.Predicting Movie Ratings/097. Problem Formulation.mp416.41MB
  98. 035.Predicting Movie Ratings/098. Content Based Recommendations.mp423.19MB
  99. 036.Collaborative Filtering/099. Collaborative Filtering.mp415.52MB
  100. 036.Collaborative Filtering/100. Collaborative Filtering Algorithm.mp414.71MB
  101. 037.Low Rank Matrix Factorization/101. Vectorization Low Rank Matrix Factorization.mp412.82MB
  102. 037.Low Rank Matrix Factorization/102. Implementational Detail Mean Normalization.mp412.91MB
  103. 038.Gradient Descent with Large Datasets/103. Learning With Large Datasets.mp48.54MB
  104. 038.Gradient Descent with Large Datasets/104. Stochastic Gradient Descent.mp420.99MB
  105. 038.Gradient Descent with Large Datasets/105. Mini-Batch Gradient Descent.mp49.75MB
  106. 038.Gradient Descent with Large Datasets/106. Stochastic Gradient Descent Convergence.mp418.11MB
  107. 039.Advanced Topics/107. Online Learning.mp420.51MB
  108. 039.Advanced Topics/108. Map Reduce and Data Parallelism.mp421.23MB
  109. 040.Photo OCR/109. Problem Description and Pipeline.mp410.42MB
  110. 040.Photo OCR/110. Sliding Windows.mp421.93MB
  111. 040.Photo OCR/111. Getting Lots of Data and Artificial Data.mp425.3MB
  112. 040.Photo OCR/112. Ceiling Analysis What Part of the Pipeline to Work on Next.mp421.92MB
  113. 041.Conclusion/113. Summary and Thank You.mp49.08MB
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