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Udemy - Math for Machine Learning [720p - WEB HD - H264 - AAC]

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  • 文件大小:571MB
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  • 最近下载:2021-04-24 21:19:03
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文件列表

  1. 16 - LDA Example 2.mp4 30MB
  2. 67 - Support Vector Classifier Example 1.mp4 24MB
  3. 22 - Maximizing the Log-Likelihood Function.mp4 23MB
  4. 15 - LDA Example 1.mp4 22MB
  5. 26 - Neural Network Model of the Output Functions.mp4 21MB
  6. 05 - Linear Algebra Solution to Least Squares Problem.mp4 20MB
  7. 04 - The Least Squares Method.mp4 19MB
  8. 55 - Maximal Margin Classifier Example 2.mp4 18MB
  9. 21 - The Multivariate Newton-Raphson Method.mp4 18MB
  10. 54 - Maximal Margin Classifier Example 1.mp4 16MB
  11. 23 - Logistic Regression Example.mp4 16MB
  12. 68 - Support Vector Classifier Example 2.mp4 16MB
  13. 45 - Proof 4.mp4 14MB
  14. 42 - Reformulating the Optimization Problem.mp4 13MB
  15. 20 - Estimating the Posterior Probability Function.mp4 13MB
  16. 11 - Modelling the Posterior Probability Functions.mp4 12MB
  17. 03 - Linear Regression.mp4 12MB
  18. 39 - Proof 1.mp4 12MB
  19. 63 - Solving the Convex Optimization Problem (Soft Margin).mp4 10MB
  20. 33 - Minimizing the Error Function Using Gradient Descent.mp4 10MB
  21. 13 - Estimating the Linear Discriminant Functions.mp4 10MB
  22. 38 - Definitions of Separating Hyperplane and Margin.mp4 9MB
  23. 12 - Linear Discriminant Functions.mp4 9MB
  24. 31 - Error Function for Binary Classification.mp4 9MB
  25. 71 - Enlarging the Feature Space.mp4 9MB
  26. 46 - Proof 5.mp4 9MB
  27. 44 - Proof 3.mp4 8MB
  28. 72 - The Kernel Trick.mp4 8MB
  29. 34 - Backpropagation Equations.mp4 7MB
  30. 06 - Example Linear Regression.mp4 7MB
  31. 28 - Choosing Activation Functions.mp4 7MB
  32. 32 - Error Function for Multiclass Classification.mp4 6MB
  33. 60 - Formulating the Optimization Problem.mp4 6MB
  34. 58 - Slack Variables Points on Correct Side of Hyperplane.mp4 6MB
  35. 02 - Course Introduction.mp4 6MB
  36. 57 - Support Vector Classifier.mp4 6MB
  37. 40 - Maximizing the Margin.mp4 6MB
  38. 10 - The Posterior Probability Functions.mp4 6MB
  39. 36 - Summary Artificial Neural Networks.mp4 6MB
  40. 01 - Course Promo.mp4 5MB
  41. 14 - Classifying Data Points Using Linear Discriminant Functions.mp4 5MB
  42. 17 - Summary Linear Discriminant Analysis.mp4 5MB
  43. 50 - Solving the Dual Problem.mp4 5MB
  44. 19 - Logistic Regression Model of the Posterior Probability Function.mp4 4MB
  45. 24 - Summary Logistic Regression.mp4 4MB
  46. 73 - Summary Support Vector Machine Classifier.mp4 4MB
  47. 62 - A Convex Optimization Problem.mp4 4MB
  48. 37 - Maximal Margin Classifier.mp4 4MB
  49. 29 - Estimating the Output Functions.mp4 3MB
  50. 30 - Error Function for Regression.mp4 3MB
  51. 64 - The Coefficients for the Soft Margin Hyperplane.mp4 3MB
  52. 48 - KKT Conditions.mp4 3MB
  53. 53 - Classifying Test Points.mp4 3MB
  54. 65 - Classifying Test Points (Soft Margin).mp4 3MB
  55. 66 - The Support Vectors (Soft Margin).mp4 3MB
  56. 35 - Summary of Backpropagation.mp4 3MB
  57. 59 - Slack Variables Points on Wrong Side of Hyperplane.mp4 2MB
  58. 69 - Summary Support Vector Classifier.mp4 2MB
  59. 49 - Primal and Dual Problems.mp4 2MB
  60. 43 - Proof 2.mp4 2MB
  61. 70 - Support Vector Machine Classifier.mp4 2MB
  62. 47 - Solving the Convex Optimization Problem.mp4 2MB
  63. 07 - Summary Linear Regression.mp4 2MB
  64. 56 - Summary Maximal Margin Classifier.mp4 2MB
  65. 27 - Forward Propagation.mp4 2MB
  66. 41 - Definition of Maximal Margin Classifier.mp4 2MB
  67. 08 - Classification.mp4 2MB
  68. 18 - Logistic Regression.mp4 2MB
  69. 52 - The Support Vectors.mp4 1MB
  70. 61 - Definition of Support Vector Classifier.mp4 1MB
  71. 09 - Linear Discriminant Analysis.mp4 967KB
  72. 25 - Artificial Neural Networks.mp4 779KB
  73. 51 - The Coefficients for the Maximal Margin Hyperplane.mp4 766KB
  74. TutsGalaxy.com.txt 41B