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| [TGx]Downloaded from torrentgalaxy.org.txt |
524б |
| 1. Basic Geometry.mp4 |
46.61Мб |
| 1. Basic Geometry.vtt |
11.41Кб |
| 1. Beginner_s Corner Section Introduction.mp4 |
34.01Мб |
| 1. Beginner_s Corner Section Introduction.vtt |
6.23Кб |
| 1. Duality Section Introduction.mp4 |
14.72Мб |
| 1. Duality Section Introduction.vtt |
4.22Кб |
| 1. Dual with Slack Variables.mp4 |
38.93Мб |
| 1. Dual with Slack Variables.vtt |
11.20Кб |
| 1. Introduction.mp4 |
16.15Мб |
| 1. Introduction.vtt |
2.69Кб |
| 1. Kernel Methods Section Introduction.mp4 |
19.13Мб |
| 1. Kernel Methods Section Introduction.vtt |
3.87Кб |
| 1. Linear SVM Section Introduction and Outline.mp4 |
17.68Мб |
| 1. Linear SVM Section Introduction and Outline.vtt |
3.74Кб |
| 1. Neural Networks Section Introduction.mp4 |
15.61Мб |
| 1. Neural Networks Section Introduction.vtt |
3.07Кб |
| 1. What is the Appendix.mp4 |
25.44Мб |
| 1. What is the Appendix.vtt |
3.29Кб |
| 10. Linear SVM Section Summary.mp4 |
18.99Мб |
| 10. Linear SVM Section Summary.vtt |
4.88Кб |
| 10. What order should I take your courses in (part 1).mp4 |
88.41Мб |
| 10. What order should I take your courses in (part 1).vtt |
14.17Кб |
| 11. What order should I take your courses in (part 2).mp4 |
123.00Мб |
| 11. What order should I take your courses in (part 2).vtt |
20.24Кб |
| 12. [Bonus] Where to get discount coupons and FREE deep learning material.mp4 |
22.49Мб |
| 12. [Bonus] Where to get discount coupons and FREE deep learning material.vtt |
2.91Кб |
| 2. Course Objectives.mp4 |
37.24Мб |
| 2. Course Objectives.vtt |
5.72Кб |
| 2. Duality and Lagrangians (part 1).mp4 |
58.69Мб |
| 2. Duality and Lagrangians (part 1).vtt |
13.63Кб |
| 2. Image Classification with SVMs.mp4 |
36.49Мб |
| 2. Image Classification with SVMs.vtt |
6.37Кб |
| 2. Linear SVM Problem Setup and Definitions.mp4 |
22.84Мб |
| 2. Linear SVM Problem Setup and Definitions.vtt |
5.11Кб |
| 2. Normal Vectors.mp4 |
14.80Мб |
| 2. Normal Vectors.vtt |
3.64Кб |
| 2. RBF Networks.mp4 |
79.54Мб |
| 2. RBF Networks.vtt |
17.03Кб |
| 2. Simple Approaches to Implementation.mp4 |
24.65Мб |
| 2. Simple Approaches to Implementation.vtt |
6.93Кб |
| 2. The Kernel Trick.mp4 |
37.25Мб |
| 2. The Kernel Trick.vtt |
8.03Кб |
| 2. Windows-Focused Environment Setup 2018.mp4 |
194.35Мб |
| 2. Windows-Focused Environment Setup 2018.vtt |
17.34Кб |
| 3. Course Outline.mp4 |
31.30Мб |
| 3. Course Outline.vtt |
6.68Кб |
| 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 |
167.01Мб |
| 3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt |
12.59Кб |
| 3. Lagrangian Duality (part 2).mp4 |
29.19Мб |
| 3. Lagrangian Duality (part 2).vtt |
6.74Кб |
| 3. Logistic Regression Review.mp4 |
39.90Мб |
| 3. Logistic Regression Review.vtt |
10.69Кб |
| 3. Margins.mp4 |
41.49Мб |
| 3. Margins.vtt |
8.56Кб |
| 3. Polynomial Kernel.mp4 |
25.37Мб |
| 3. Polynomial Kernel.vtt |
5.91Кб |
| 3. RBF Approximations.mp4 |
44.41Мб |
| 3. RBF Approximations.vtt |
9.37Кб |
| 3. Spam Detection with SVMs.mp4 |
101.47Мб |
| 3. Spam Detection with SVMs.vtt |
12.42Кб |
| 3. SVM with Projected Gradient Descent Code.mp4 |
83.60Мб |
| 3. SVM with Projected Gradient Descent Code.vtt |
7.80Кб |
| 4. Gaussian Kernel.mp4 |
26.96Мб |
| 4. Gaussian Kernel.vtt |
5.26Кб |
| 4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 |
117.69Мб |
| 4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt |
27.68Кб |
| 4. Kernel SVM Gradient Descent with Primal (Theory).mp4 |
21.35Мб |
| 4. Kernel SVM Gradient Descent with Primal (Theory).vtt |
4.89Кб |
| 4. Linear SVM Objective.mp4 |
49.17Мб |
| 4. Linear SVM Objective.vtt |
11.64Кб |
| 4. Loss Function and Regularization.mp4 |
16.15Мб |
| 4. Loss Function and Regularization.vtt |
4.30Кб |
| 4. Medical Diagnosis with SVMs.mp4 |
47.91Мб |
| 4. Medical Diagnosis with SVMs.vtt |
6.05Кб |
| 4. Relationship to Linear Programming.mp4 |
20.12Мб |
| 4. Relationship to Linear Programming.vtt |
4.55Кб |
| 4. What Happened to Infinite Dimensionality.mp4 |
12.57Мб |
| 4. What Happened to Infinite Dimensionality.vtt |
2.90Кб |
| 4. Where to get the code and data.mp4 |
39.03Мб |
| 4. Where to get the code and data.vtt |
6.98Кб |
| 5. Build Your Own RBF Network.mp4 |
39.11Мб |
| 5. Build Your Own RBF Network.vtt |
3.98Кб |
| 5. How to Succeed in this Course (Long Version).mp4 |
39.25Мб |
| 5. How to Succeed in this Course (Long Version).vtt |
12.83Кб |
| 5. Kernel SVM Gradient Descent with Primal (Code).mp4 |
58.72Мб |
| 5. Kernel SVM Gradient Descent with Primal (Code).vtt |
4.09Кб |
| 5. Linear and Quadratic Programming.mp4 |
64.22Мб |
| 5. Linear and Quadratic Programming.vtt |
13.19Кб |
| 5. Prediction Confidence.mp4 |
30.65Мб |
| 5. Prediction Confidence.vtt |
7.92Кб |
| 5. Predictions and Support Vectors.mp4 |
38.88Мб |
| 5. Predictions and Support Vectors.vtt |
9.57Кб |
| 5. Regression with SVMs.mp4 |
50.90Мб |
| 5. Regression with SVMs.vtt |
5.63Кб |
| 5. Using the Gaussian Kernel.mp4 |
36.01Мб |
| 5. Using the Gaussian Kernel.vtt |
7.64Кб |
| 6. Cross-Validation.mp4 |
54.63Мб |
| 6. Cross-Validation.vtt |
8.33Кб |
| 6. How to Code by Yourself (part 1).mp4 |
82.57Мб |
| 6. How to Code by Yourself (part 1).vtt |
19.38Кб |
| 6. Nonlinear Problems.mp4 |
47.05Мб |
| 6. Nonlinear Problems.vtt |
10.41Кб |
| 6. Relationship to Deep Learning Neural Networks.mp4 |
33.75Мб |
| 6. Relationship to Deep Learning Neural Networks.vtt |
7.78Кб |
| 6. Slack Variables.mp4 |
38.68Мб |
| 6. Slack Variables.vtt |
7.95Кб |
| 6. SMO (Sequential Minimal Optimization).mp4 |
41.42Мб |
| 6. SMO (Sequential Minimal Optimization).vtt |
10.54Кб |
| 6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp4 |
19.85Мб |
| 6. Why does the Gaussian Kernel correspond to infinite-dimensional features.vtt |
4.40Кб |
| 6. Why Transform Primal to Dual.mp4 |
16.93Мб |
| 6. Why Transform Primal to Dual.vtt |
3.75Кб |
| 7. Duality Section Conclusion.mp4 |
13.22Мб |
| 7. Duality Section Conclusion.vtt |
2.99Кб |
| 7. Hinge Loss (and its Relationship to Logistic Regression).mp4 |
29.69Мб |
| 7. Hinge Loss (and its Relationship to Logistic Regression).vtt |
6.68Кб |
| 7. How do you get the data How do you process the data.mp4 |
28.83Мб |
| 7. How do you get the data How do you process the data.vtt |
6.68Кб |
| 7. How to Code by Yourself (part 2).mp4 |
56.69Мб |
| 7. How to Code by Yourself (part 2).vtt |
11.44Кб |
| 7. Linear Classifiers Section Conclusion.mp4 |
19.29Мб |
| 7. Linear Classifiers Section Conclusion.vtt |
4.69Кб |
| 7. Neural Network-SVM Mashup.mp4 |
72.29Мб |
| 7. Neural Network-SVM Mashup.vtt |
7.27Кб |
| 7. Other Kernels.mp4 |
32.44Мб |
| 7. Other Kernels.vtt |
7.23Кб |
| 7. Support Vector Regression.mp4 |
27.24Мб |
| 7. Support Vector Regression.vtt |
5.82Кб |
| 8. Linear SVM with Gradient Descent.mp4 |
15.68Мб |
| 8. Linear SVM with Gradient Descent.vtt |
3.13Кб |
| 8. Mercer_s Condition.mp4 |
27.57Мб |
| 8. Mercer_s Condition.vtt |
6.57Кб |
| 8. Multiclass Classification.mp4 |
19.08Мб |
| 8. Multiclass Classification.vtt |
4.89Кб |
| 8. Neural Networks Section Conclusion.mp4 |
11.83Мб |
| 8. Neural Networks Section Conclusion.vtt |
2.83Кб |
| 8. Proof that using Jupyter Notebook is the same as not using it.mp4 |
78.29Мб |
| 8. Proof that using Jupyter Notebook is the same as not using it.vtt |
12.31Кб |
| 9. Kernel Methods Section Summary.mp4 |
11.14Мб |
| 9. Kernel Methods Section Summary.vtt |
2.82Кб |
| 9. Linear SVM with Gradient Descent (Code).mp4 |
51.93Мб |
| 9. Linear SVM with Gradient Descent (Code).vtt |
5.31Кб |
| 9. Python 2 vs Python 3.mp4 |
30.25Мб |
| 9. Python 2 vs Python 3.vtt |
5.35Кб |
| Discuss.FTUForum.com.html |
31.89Кб |
| FreeCoursesOnline.Me.html |
108.30Кб |
| FTUForum.com.html |
100.44Кб |
| How you can help Team-FTU.txt |
235б |
| Torrent Downloaded From GloDls.to.txt |
84б |