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Stanford机器学习课程

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视频 2017-9-30 18:51 2024-10-12 09:15 171 1.32 GB 114
磁力链接
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迅雷链接
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Stanford机器学习课程的二维码
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Stanford机器学习课程

文件列表
  1. 12 - 6 - Using An SVM (21 min).mkv23.63MB
  2. 12 - 3 - Mathematics Behind Large Margin Classification (Optional) (20 min).mkv21.51MB
  3. 5 - 2 - Moving Data Around (16 min).mkv20.52MB
  4. 18 - 3 - Getting Lots of Data and Artificial Data (16 min).mkv18.57MB
  5. 6 - 6 - Advanced Optimization (14 min).mkv17.95MB
  6. 14 - 4 - Principal Component Analysis Algorithm (15 min).mkv17.55MB
  7. 5 - 1 - Basic Operations (14 min).mkv17.5MB
  8. 12 - 4 - Kernels I (16 min).mkv17.32MB
  9. 12 - 5 - Kernels II (16 min).mkv17.2MB
  10. 4 - 6 - Normal Equation (16 min).mkv16.88MB
  11. 16 - 2 - Content Based Recommendations (15 min).mkv16.71MB
  12. 6 - 3 - Decision Boundary (15 min).mkv16.51MB
  13. 1 - 4 - Unsupervised Learning (14 min).mkv16.45MB
  14. 12 - 1 - Optimization Objective (15 min).mkv16.42MB
  15. 18 - 2 - Sliding Windows (15 min).mkv16.3MB
  16. 5 - 5 - Control Statements_ for, while, if statements (13 min).mkv16.29MB
  17. 15 - 8 - Anomaly Detection using the Multivariate Gaussian Distribution (Optional) (14 min).mkv16.12MB
  18. 9 - 7 - Putting It Together (14 min).mkv16.1MB
  19. 18 - 4 - Ceiling Analysis_ What Part of the Pipeline to Work on Next (14 min).mkv15.9MB
  20. 5 - 6 - Vectorization (14 min).mkv15.88MB
  21. 17 - 6 - Map Reduce and Data Parallelism (14 min).mkv15.84MB
  22. 11 - 4 - Trading Off Precision and Recall (14 min).mkv15.77MB
  23. 15 - 7 - Multivariate Gaussian Distribution (Optional) (14 min).mkv15.72MB
  24. 9 - 3 - Backpropagation Intuition (13 min).mkv15.25MB
  25. 11 - 2 - Error Analysis (13 min).mkv15.22MB
  26. 17 - 2 - Stochastic Gradient Descent (13 min).mkv15.12MB
  27. 5 - 3 - Computing on Data (13 min).mkv15.04MB
  28. 15 - 4 - Developing and Evaluating an Anomaly Detection System (13 min).mkv14.96MB
  29. 10 - 3 - Model Selection and Train_Validation_Test Sets (12 min).mkv14.92MB
  30. 9 - 8 - Autonomous Driving (7 min).mkv14.79MB
  31. 3 - 3 - Matrix Vector Multiplication (14 min).mkv14.78MB
  32. 17 - 5 - Online Learning (13 min).mkv14.72MB
  33. 14 - 7 - Advice for Applying PCA (13 min).mkv14.5MB
  34. 14 - 1 - Motivation I_ Data Compression (10 min).mkv14.15MB
  35. 15 - 6 - Choosing What Features to Use (12 min).mkv13.93MB
  36. 8 - 6 - Examples and Intuitions II (10 min).mkv13.84MB
  37. 15 - 3 - Algorithm (12 min).mkv13.77MB
  38. 9 - 2 - Backpropagation Algorithm (12 min).mkv13.75MB
  39. 13 - 2 - K-Means Algorithm (13 min).mkv13.61MB
  40. 8 - 3 - Model Representation I (12 min).mkv13.32MB
  41. 9 - 5 - Gradient Checking (12 min).mkv13.32MB
  42. 2 - 5 - Gradient Descent (11 min).mkv13.32MB
  43. 8 - 4 - Model Representation II (12 min).mkv13.27MB
  44. 1 - 3 - Supervised Learning (12 min).mkv13.25MB
  45. 5 - 4 - Plotting Data (10 min).mkv13.17MB
  46. 17 - 4 - Stochastic Gradient Descent Convergence (12 min).mkv13.15MB
  47. 11 - 3 - Error Metrics for Skewed Classes (12 min).mkv13.07MB
  48. 6 - 4 - Cost Function (11 min).mkv12.92MB
  49. 2 - 6 - Gradient Descent Intuition (12 min).mkv12.84MB
  50. 10 - 6 - Learning Curves (12 min).mkv12.74MB
  51. 11 - 5 - Data For Machine Learning (11 min).mkv12.7MB
  52. 3 - 6 - Inverse and Transpose (11 min).mkv12.69MB
  53. 3 - 4 - Matrix Matrix Multiplication (11 min).mkv12.42MB
  54. 10 - 5 - Regularization and Bias_Variance (11 min).mkv12.42MB
  55. 2 - 3 - Cost Function - Intuition I (11 min).mkv12.06MB
  56. 2 - 7 - GradientDescentForLinearRegression (6 min).mkv12.02MB
  57. 7 - 3 - Regularized Linear Regression (11 min).mkv11.84MB
  58. 6 - 5 - Simplified Cost Function and Gradient Descent (10 min).mkv11.8MB
  59. 1 - 1 - Welcome (7 min).mkv11.69MB
  60. 14 - 5 - Choosing the Number of Principal Components (11 min).mkv11.67MB
  61. 12 - 2 - Large Margin Intuition (11 min).mkv11.65MB
  62. 16 - 3 - Collaborative Filtering (10 min).mkv11.6MB
  63. 15 - 2 - Gaussian Distribution (10 min).mkv11.53MB
  64. 7 - 2 - Cost Function (10 min).mkv11.48MB
  65. 2 - 4 - Cost Function - Intuition II (9 min).mkv11.22MB
  66. 11 - 1 - Prioritizing What to Work On (10 min).mkv11.03MB
  67. 7 - 1 - The Problem of Overfitting (10 min).mkv11MB
  68. 7 - 4 - Regularized Logistic Regression (9 min).mkv10.77MB
  69. 8 - 1 - Non-linear Hypotheses (10 min).mkv10.73MB
  70. 16 - 1 - Problem Formulation (8 min).mkv10.57MB
  71. 14 - 3 - Principal Component Analysis Problem Formulation (9 min).mkv10.32MB
  72. 16 - 4 - Collaborative Filtering Algorithm (9 min).mkv10.18MB
  73. 8 - 2 - Neurons and the Brain (8 min).mkv9.77MB
  74. 3 - 5 - Matrix Multiplication Properties (9 min).mkv9.67MB
  75. 16 - 6 - Implementational Detail_ Mean Normalization (9 min).mkv9.58MB
  76. 16 - 5 - Vectorization_ Low Rank Matrix Factorization (8 min).mkv9.55MB
  77. 3 - 1 - Matrices and Vectors (9 min).mkv9.42MB
  78. 4 - 3 - Gradient Descent in Practice I - Feature Scaling (9 min).mkv9.32MB
  79. 13 - 5 - Choosing the Number of Clusters (8 min).mkv9.28MB
  80. 9 - 4 - Implementation Note_ Unrolling Parameters (8 min).mkv9.27MB
  81. 1 - 2 - What is Machine Learning_ (7 min).mkv9.25MB
  82. 15 - 5 - Anomaly Detection vs. Supervised Learning (8 min).mkv9.17MB
  83. 4 - 4 - Gradient Descent in Practice II - Learning Rate (9 min).mkv9.13MB
  84. 2 - 2 - Cost Function (8 min).mkv8.91MB
  85. 2 - 1 - Model Representation (8 min).mkv8.86MB
  86. 10 - 4 - Diagnosing Bias vs. Variance (8 min).mkv8.86MB
  87. 4 - 1 - Multiple Features (8 min).mkv8.71MB
  88. 6 - 1 - Classification (8 min).mkv8.65MB
  89. 13 - 4 - Random Initialization (8 min).mkv8.56MB
  90. 10 - 2 - Evaluating a Hypothesis (8 min).mkv8.36MB
  91. 15 - 1 - Problem Motivation (8 min).mkv8.23MB
  92. 6 - 2 - Hypothesis Representation (7 min).mkv8.23MB
  93. 4 - 5 - Features and Polynomial Regression (8 min).mkv8.15MB
  94. 10 - 7 - Deciding What to Do Next Revisited (7 min).mkv8.08MB
  95. 13 - 3 - Optimization Objective (7 min)(1).mkv8.04MB
  96. 13 - 3 - Optimization Objective (7 min).mkv8.03MB
  97. 18 - 1 - Problem Description and Pipeline (7 min).mkv7.81MB
  98. 8 - 5 - Examples and Intuitions I (7 min).mkv7.78MB
  99. 9 - 1 - Cost Function (7 min).mkv7.56MB
  100. 9 - 6 - Random Initialization (7 min).mkv7.46MB
  101. 3 - 2 - Addition and Scalar Multiplication (7 min).mkv7.35MB
  102. 17 - 3 - Mini-Batch Gradient Descent (6 min).mkv7.22MB
  103. 6 - 7 - Multiclass Classification_ One-vs-all (6 min).mkv6.83MB
  104. 10 - 1 - Deciding What to Try Next (6 min).mkv6.78MB
  105. 17 - 1 - Learning With Large Datasets (6 min).mkv6.41MB
  106. 14 - 2 - Motivation II_ Visualization (6 min).mkv6.22MB
  107. 4 - 7 - Normal Equation Noninvertibility (Optional) (6 min).mkv6.15MB
  108. 19 - 1 - Summary and Thank You (5 min).mkv6.02MB
  109. 2 - 8 - What_'s Next (6 min).mkv5.99MB
  110. 4 - 2 - Gradient Descent for Multiple Variables (5 min).mkv5.71MB
  111. 5 - 7 - Working on and Submitting Programming Exercises (4 min).mkv5.41MB
  112. 14 - 6 - Reconstruction from Compressed Representation (4 min).mkv4.92MB
  113. 8 - 7 - Multiclass Classification (4 min).mkv4.77MB
  114. 13 - 1 - Unsupervised Learning_ Introduction (3 min).mkv3.76MB
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