[FreeCoursesOnline Me] [Stone River eLearning] Math for Machine Learning - [FCO]
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| Torrent Hash : | 049F9792733CA74149FBD439DEDBEFCD218ABBAD |
| Torrent Added : | 1 Year+ in Other - Other |
| Torrent Size : | 521.3 MB |
[FreeCoursesOnline Me] [Stone River eLearning] Math for Machine Learning - [FCO]
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Torrent File Content (79 files)
[FreeCoursesOnline.Me] [Stone River eLearning] Math for Machine Learning - [FCO]
01.Course Promo.mp4 -
01.Course Promo.mp4 -
4.67 MB
02.Course Introduction.mp4 -
5.18 MB
03.Linear Regression.mp4 -
10.97 MB
04.The Least Squares Method.mp4 -
17.54 MB
05.Linear Algebra Solution to Least Squares Problem.mp4 -
17.91 MB
06.Example Linear Regression.mp4 -
6 MB
07.Summary Linear Regression.mp4 -
1.61 MB
08.Classification.mp4 -
1.67 MB
09.Linear Discriminant Analysis.mp4 -
967.07 KB
10.The Posterior Probability Functions.mp4 -
5.29 MB
11.Modelling the Posterior Probability Functions.mp4 -
11.32 MB
12.Linear Discriminant Functions.mp4 -
8.38 MB
13.Estimating the Linear Discriminant Functions.mp4 -
8.59 MB
14.Classifying Data Points Using Linear Discriminant Functions.mp4 -
4.95 MB
15.LDA Example 1.mp4 -
20.16 MB
16.LDA Example 2.mp4 -
26.94 MB
17.Summary Linear Discriminant Analysis.mp4 -
4.83 MB
18.Logistic Regression.mp4 -
1.63 MB
19.Logistic Regression Model of the Posterior Probability Function.mp4 -
4.22 MB
20.Estimating the Posterior Probability Function.mp4 -
12.92 MB
21.The Multivariate Newton-Raphson Method.mp4 -
16.62 MB
22.Maximizing the Log-Likelihood Function.mp4 -
21.45 MB
23.Logistic Regression Example.mp4 -
14.3 MB
24.Summary Logistic Regression.mp4 -
3.75 MB
25.Artificial Neural Networks.mp4 -
778.55 KB
26.Neural Network Model of the Output Functions.mp4 -
18.79 MB
27.Forward Propagation.mp4 -
1.61 MB
28.Choosing Activation Functions.mp4 -
5.91 MB
29.Estimating the Output Functions.mp4 -
3.02 MB
30.Error Function for Regression.mp4 -
3.34 MB
31.Error Function for Binary Classification.mp4 -
8.14 MB
32.Error Function for Multiclass Classification.mp4 -
6.26 MB
33.Minimizing the Error Function Using Gradient Descent.mp4 -
9.21 MB
34.Backpropagation Equations.mp4 -
6.15 MB
35.Summary of Backpropagation.mp4 -
2.31 MB
36.Summary Artificial Neural Networks.mp4 -
5.02 MB
37.Maximal Margin Classifier.mp4 -
3.11 MB
38.Definitions of Separating Hyperplane and Margin.mp4 -
8.45 MB
39.Proof 1.mp4 -
10.84 MB
40.Maximizing the Margin.mp4 -
5.34 MB
41.Definition of Maximal Margin Classifier.mp4 -
1.54 MB
42.Reformulating the Optimization Problem.mp4 -
12.22 MB
43.Proof 2.mp4 -
1.81 MB
44.Proof 3.mp4 -
7.29 MB
45.Proof 4.mp4 -
12.97 MB
46.Proof 5.mp4 -
8.14 MB
47.Solving the Convex Optimization Problem.mp4 -
1.65 MB
48.KKT Conditions.mp4 -
2.72 MB
49.Primal and Dual Problems.mp4 -
2.07 MB
50.Solving the Dual Problem.mp4 -
4.79 MB
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