[FTUForum.com] [UDEMY] Machine Learning and AI Support Vector Machines in Python [FTU]

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Last Access Time	Dec. 1, 2024, 12:33 a.m.
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9. Appendix/2. Windows-Focused Environment Setup 2018.mp4 194.3 MB
9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 167.0 MB
9. Appendix/11. What order should I take your courses in (part 2).mp4 123.0 MB
9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 117.7 MB
2. Beginner_s Corner/3. Spam Detection with SVMs.mp4 101.5 MB
9. Appendix/10. What order should I take your courses in (part 1).mp4 88.4 MB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.mp4 83.6 MB
9. Appendix/6. How to Code by Yourself (part 1).mp4 82.6 MB
8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.mp4 79.5 MB
9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78.3 MB
8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.mp4 72.3 MB
4. Linear SVM/5. Linear and Quadratic Programming.mp4 64.2 MB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).mp4 58.7 MB
5. Duality/2. Duality and Lagrangians (part 1).mp4 58.7 MB
9. Appendix/7. How to Code by Yourself (part 2).mp4 56.7 MB
2. Beginner_s Corner/6. Cross-Validation.mp4 54.6 MB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).mp4 51.9 MB
2. Beginner_s Corner/5. Regression with SVMs.mp4 50.9 MB
4. Linear SVM/4. Linear SVM Objective.mp4 49.2 MB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.mp4 47.9 MB
3. Review of Linear Classifiers/6. Nonlinear Problems.mp4 47.0 MB
3. Review of Linear Classifiers/1. Basic Geometry.mp4 46.6 MB
8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.mp4 44.4 MB
4. Linear SVM/3. Margins.mp4 41.5 MB
7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).mp4 41.4 MB
3. Review of Linear Classifiers/3. Logistic Regression Review.mp4 39.9 MB
9. Appendix/5. How to Succeed in this Course (Long Version).mp4 39.3 MB
8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.mp4 39.1 MB
1. Welcome/4. Where to get the code and data.mp4 39.0 MB
7. Implementations and Extensions/1. Dual with Slack Variables.mp4 38.9 MB
5. Duality/5. Predictions and Support Vectors.mp4 38.9 MB
4. Linear SVM/6. Slack Variables.mp4 38.7 MB
6. Kernel Methods/2. The Kernel Trick.mp4 37.2 MB
1. Welcome/2. Course Objectives.mp4 37.2 MB
2. Beginner_s Corner/2. Image Classification with SVMs.mp4 36.5 MB
6. Kernel Methods/5. Using the Gaussian Kernel.mp4 36.0 MB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.mp4 34.0 MB
8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.mp4 33.8 MB
6. Kernel Methods/7. Other Kernels.mp4 32.4 MB
1. Welcome/3. Course Outline.mp4 31.3 MB
3. Review of Linear Classifiers/5. Prediction Confidence.mp4 30.6 MB
9. Appendix/9. Python 2 vs Python 3.mp4 30.3 MB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).mp4 29.7 MB
5. Duality/3. Lagrangian Duality (part 2).mp4 29.2 MB
2. Beginner_s Corner/7. How do you get the data How do you process the data.mp4 28.8 MB
6. Kernel Methods/8. Mercer_s Condition.mp4 27.6 MB
7. Implementations and Extensions/7. Support Vector Regression.mp4 27.2 MB
6. Kernel Methods/4. Gaussian Kernel.mp4 27.0 MB
9. Appendix/1. What is the Appendix.mp4 25.4 MB
6. Kernel Methods/3. Polynomial Kernel.mp4 25.4 MB
7. Implementations and Extensions/2. Simple Approaches to Implementation.mp4 24.7 MB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.mp4 22.8 MB
9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.mp4 22.5 MB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).mp4 21.3 MB
5. Duality/4. Relationship to Linear Programming.mp4 20.1 MB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp4 19.8 MB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.mp4 19.3 MB
6. Kernel Methods/1. Kernel Methods Section Introduction.mp4 19.1 MB
7. Implementations and Extensions/8. Multiclass Classification.mp4 19.1 MB
4. Linear SVM/10. Linear SVM Section Summary.mp4 19.0 MB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.mp4 17.7 MB
5. Duality/6. Why Transform Primal to Dual.mp4 16.9 MB
3. Review of Linear Classifiers/4. Loss Function and Regularization.mp4 16.1 MB
1. Welcome/1. Introduction.mp4 16.1 MB
4. Linear SVM/8. Linear SVM with Gradient Descent.mp4 15.7 MB
8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.mp4 15.6 MB
3. Review of Linear Classifiers/2. Normal Vectors.mp4 14.8 MB
5. Duality/1. Duality Section Introduction.mp4 14.7 MB
5. Duality/7. Duality Section Conclusion.mp4 13.2 MB
8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.mp4 12.6 MB
8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.mp4 11.8 MB
6. Kernel Methods/9. Kernel Methods Section Summary.mp4 11.1 MB
FreeCoursesOnline.Me.html 108.3 KB
FTUForum.com.html 100.4 KB
Discuss.FTUForum.com.html 31.9 KB
9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.7 KB
9. Appendix/11. What order should I take your courses in (part 2).vtt 20.2 KB
9. Appendix/6. How to Code by Yourself (part 1).vtt 19.4 KB
9. Appendix/2. Windows-Focused Environment Setup 2018.vtt 17.3 KB
8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.vtt 17.0 KB
9. Appendix/10. What order should I take your courses in (part 1).vtt 14.2 KB
5. Duality/2. Duality and Lagrangians (part 1).vtt 13.6 KB
4. Linear SVM/5. Linear and Quadratic Programming.vtt 13.2 KB
9. Appendix/5. How to Succeed in this Course (Long Version).vtt 12.8 KB
9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.6 KB
2. Beginner_s Corner/3. Spam Detection with SVMs.vtt 12.4 KB
9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 12.3 KB
4. Linear SVM/4. Linear SVM Objective.vtt 11.6 KB
9. Appendix/7. How to Code by Yourself (part 2).vtt 11.4 KB
3. Review of Linear Classifiers/1. Basic Geometry.vtt 11.4 KB
7. Implementations and Extensions/1. Dual with Slack Variables.vtt 11.2 KB
3. Review of Linear Classifiers/3. Logistic Regression Review.vtt 10.7 KB
7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).vtt 10.5 KB
3. Review of Linear Classifiers/6. Nonlinear Problems.vtt 10.4 KB
5. Duality/5. Predictions and Support Vectors.vtt 9.6 KB
8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.vtt 9.4 KB
4. Linear SVM/3. Margins.vtt 8.6 KB
2. Beginner_s Corner/6. Cross-Validation.vtt 8.3 KB
6. Kernel Methods/2. The Kernel Trick.vtt 8.0 KB
4. Linear SVM/6. Slack Variables.vtt 7.9 KB
3. Review of Linear Classifiers/5. Prediction Confidence.vtt 7.9 KB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.vtt 7.8 KB
8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.vtt 7.8 KB
6. Kernel Methods/5. Using the Gaussian Kernel.vtt 7.6 KB
8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.vtt 7.3 KB
6. Kernel Methods/7. Other Kernels.vtt 7.2 KB
1. Welcome/4. Where to get the code and data.vtt 7.0 KB
7. Implementations and Extensions/2. Simple Approaches to Implementation.vtt 6.9 KB
5. Duality/3. Lagrangian Duality (part 2).vtt 6.7 KB
2. Beginner_s Corner/7. How do you get the data How do you process the data.vtt 6.7 KB
1. Welcome/3. Course Outline.vtt 6.7 KB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).vtt 6.7 KB
6. Kernel Methods/8. Mercer_s Condition.vtt 6.6 KB
2. Beginner_s Corner/2. Image Classification with SVMs.vtt 6.4 KB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.vtt 6.2 KB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.vtt 6.0 KB
6. Kernel Methods/3. Polynomial Kernel.vtt 5.9 KB
7. Implementations and Extensions/7. Support Vector Regression.vtt 5.8 KB
1. Welcome/2. Course Objectives.vtt 5.7 KB
2. Beginner_s Corner/5. Regression with SVMs.vtt 5.6 KB
9. Appendix/9. Python 2 vs Python 3.vtt 5.4 KB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).vtt 5.3 KB
6. Kernel Methods/4. Gaussian Kernel.vtt 5.3 KB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.vtt 5.1 KB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).vtt 4.9 KB
7. Implementations and Extensions/8. Multiclass Classification.vtt 4.9 KB
4. Linear SVM/10. Linear SVM Section Summary.vtt 4.9 KB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.vtt 4.7 KB
5. Duality/4. Relationship to Linear Programming.vtt 4.6 KB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.vtt 4.4 KB
3. Review of Linear Classifiers/4. Loss Function and Regularization.vtt 4.3 KB
5. Duality/1. Duality Section Introduction.vtt 4.2 KB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).vtt 4.1 KB
8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.vtt 4.0 KB
6. Kernel Methods/1. Kernel Methods Section Introduction.vtt 3.9 KB
5. Duality/6. Why Transform Primal to Dual.vtt 3.8 KB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.vtt 3.7 KB
3. Review of Linear Classifiers/2. Normal Vectors.vtt 3.6 KB
9. Appendix/1. What is the Appendix.vtt 3.3 KB
4. Linear SVM/8. Linear SVM with Gradient Descent.vtt 3.1 KB
8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.vtt 3.1 KB
5. Duality/7. Duality Section Conclusion.vtt 3.0 KB
9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.vtt 2.9 KB
8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.vtt 2.9 KB
8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.vtt 2.8 KB
6. Kernel Methods/9. Kernel Methods Section Summary.vtt 2.8 KB
1. Welcome/1. Introduction.vtt 2.7 KB
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