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Math for Machine Learning

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视频 2020-6-17 06:12 2024-11-24 07:09 71 571.26 MB 73
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Math for Machine Learning

文件列表
  1. Math for Machine Learning/16 - LDA Example 2.mp429.58MB
  2. Math for Machine Learning/01 - Course Promo.mp45.45MB
  3. Math for Machine Learning/02 - Course Introduction.mp45.97MB
  4. Math for Machine Learning/03 - Linear Regression.mp412.11MB
  5. Math for Machine Learning/04 - The Least Squares Method.mp419.22MB
  6. Math for Machine Learning/05 - Linear Algebra Solution to Least Squares Problem.mp419.81MB
  7. Math for Machine Learning/06 - Example Linear Regression.mp46.6MB
  8. Math for Machine Learning/07 - Summary Linear Regression.mp41.8MB
  9. Math for Machine Learning/08 - Classification.mp41.67MB
  10. Math for Machine Learning/09 - Linear Discriminant Analysis.mp4967.07KB
  11. Math for Machine Learning/10 - The Posterior Probability Functions.mp45.86MB
  12. Math for Machine Learning/11 - Modelling the Posterior Probability Functions.mp412.45MB
  13. Math for Machine Learning/12 - Linear Discriminant Functions.mp49.23MB
  14. Math for Machine Learning/13 - Estimating the Linear Discriminant Functions.mp49.54MB
  15. Math for Machine Learning/14 - Classifying Data Points Using Linear Discriminant Functions.mp45.43MB
  16. Math for Machine Learning/15 - LDA Example 1.mp422.25MB
  17. Math for Machine Learning/17 - Summary Linear Discriminant Analysis.mp45.36MB
  18. Math for Machine Learning/18 - Logistic Regression.mp41.63MB
  19. Math for Machine Learning/19 - Logistic Regression Model of the Posterior Probability Function.mp44.22MB
  20. Math for Machine Learning/20 - Estimating the Posterior Probability Function.mp412.92MB
  21. Math for Machine Learning/21 - The Multivariate Newton-Raphson Method.mp418.01MB
  22. Math for Machine Learning/22 - Maximizing the Log-Likelihood Function.mp423.48MB
  23. Math for Machine Learning/23 - Logistic Regression Example.mp415.78MB
  24. Math for Machine Learning/24 - Summary Logistic Regression.mp44.19MB
  25. Math for Machine Learning/25 - Artificial Neural Networks.mp4778.55KB
  26. Math for Machine Learning/26 - Neural Network Model of the Output Functions.mp420.71MB
  27. Math for Machine Learning/27 - Forward Propagation.mp41.75MB
  28. Math for Machine Learning/28 - Choosing Activation Functions.mp46.59MB
  29. Math for Machine Learning/29 - Estimating the Output Functions.mp43.36MB
  30. Math for Machine Learning/30 - Error Function for Regression.mp43.34MB
  31. Math for Machine Learning/31 - Error Function for Binary Classification.mp49.07MB
  32. Math for Machine Learning/32 - Error Function for Multiclass Classification.mp46.26MB
  33. Math for Machine Learning/33 - Minimizing the Error Function Using Gradient Descent.mp410.18MB
  34. Math for Machine Learning/34 - Backpropagation Equations.mp46.79MB
  35. Math for Machine Learning/35 - Summary of Backpropagation.mp42.54MB
  36. Math for Machine Learning/36 - Summary Artificial Neural Networks.mp45.6MB
  37. Math for Machine Learning/37 - Maximal Margin Classifier.mp43.51MB
  38. Math for Machine Learning/38 - Definitions of Separating Hyperplane and Margin.mp49.3MB
  39. Math for Machine Learning/39 - Proof 1.mp411.85MB
  40. Math for Machine Learning/40 - Maximizing the Margin.mp45.89MB
  41. Math for Machine Learning/41 - Definition of Maximal Margin Classifier.mp41.7MB
  42. Math for Machine Learning/42 - Reformulating the Optimization Problem.mp413.38MB
  43. Math for Machine Learning/43 - Proof 2.mp42MB
  44. Math for Machine Learning/44 - Proof 3.mp48.03MB
  45. Math for Machine Learning/45 - Proof 4.mp414.26MB
  46. Math for Machine Learning/46 - Proof 5.mp48.92MB
  47. Math for Machine Learning/47 - Solving the Convex Optimization Problem.mp41.83MB
  48. Math for Machine Learning/48 - KKT Conditions.mp42.96MB
  49. Math for Machine Learning/49 - Primal and Dual Problems.mp42.07MB
  50. Math for Machine Learning/50 - Solving the Dual Problem.mp45.32MB
  51. Math for Machine Learning/51 - The Coefficients for the Maximal Margin Hyperplane.mp4765.95KB
  52. Math for Machine Learning/52 - The Support Vectors.mp41.49MB
  53. Math for Machine Learning/53 - Classifying Test Points.mp42.77MB
  54. Math for Machine Learning/54 - Maximal Margin Classifier Example 1.mp415.87MB
  55. Math for Machine Learning/55 - Maximal Margin Classifier Example 2.mp418.49MB
  56. Math for Machine Learning/56 - Summary Maximal Margin Classifier.mp41.76MB
  57. Math for Machine Learning/57 - Support Vector Classifier.mp45.94MB
  58. Math for Machine Learning/58 - Slack Variables Points on Correct Side of Hyperplane.mp46.05MB
  59. Math for Machine Learning/59 - Slack Variables Points on Wrong Side of Hyperplane.mp42.43MB
  60. Math for Machine Learning/60 - Formulating the Optimization Problem.mp46.05MB
  61. Math for Machine Learning/61 - Definition of Support Vector Classifier.mp41.2MB
  62. Math for Machine Learning/62 - A Convex Optimization Problem.mp43.59MB
  63. Math for Machine Learning/63 - Solving the Convex Optimization Problem (Soft Margin).mp410.4MB
  64. Math for Machine Learning/64 - The Coefficients for the Soft Margin Hyperplane.mp43.25MB
  65. Math for Machine Learning/65 - Classifying Test Points (Soft Margin).mp42.63MB
  66. Math for Machine Learning/66 - The Support Vectors (Soft Margin).mp42.57MB
  67. Math for Machine Learning/67 - Support Vector Classifier Example 1.mp424.35MB
  68. Math for Machine Learning/68 - Support Vector Classifier Example 2.mp415.74MB
  69. Math for Machine Learning/69 - Summary Support Vector Classifier.mp42.18MB
  70. Math for Machine Learning/70 - Support Vector Machine Classifier.mp41.87MB
  71. Math for Machine Learning/71 - Enlarging the Feature Space.mp49.05MB
  72. Math for Machine Learning/72 - The Kernel Trick.mp47.56MB
  73. Math for Machine Learning/73 - Summary Support Vector Machine Classifier.mp43.78MB
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