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

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文档列表

9. Appendix/2. Windows-Focused Environment Setup 2018.mp4 203.8 MB
9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 175.1 MB
9. Appendix/11. What order should I take your courses in (part 2).mp4 129.0 MB
9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 123.4 MB
2. Beginner_s Corner/3. Spam Detection with SVMs.mp4 106.4 MB
9. Appendix/10. What order should I take your courses in (part 1).mp4 92.7 MB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.mp4 87.7 MB
9. Appendix/6. How to Code by Yourself (part 1).mp4 86.6 MB
8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.mp4 83.4 MB
9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.mp4 75.8 MB
4. Linear SVM/5. Linear and Quadratic Programming.mp4 67.3 MB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).mp4 61.6 MB
5. Duality/2. Duality and Lagrangians (part 1).mp4 61.5 MB
9. Appendix/7. How to Code by Yourself (part 2).mp4 59.4 MB
2. Beginner_s Corner/6. Cross-Validation.mp4 57.3 MB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).mp4 54.5 MB
2. Beginner_s Corner/5. Regression with SVMs.mp4 53.4 MB
4. Linear SVM/4. Linear SVM Objective.mp4 51.6 MB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.mp4 50.2 MB
3. Review of Linear Classifiers/6. Nonlinear Problems.mp4 49.3 MB
3. Review of Linear Classifiers/1. Basic Geometry.mp4 48.9 MB
8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.mp4 46.6 MB
4. Linear SVM/3. Margins.mp4 43.5 MB
7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).mp4 43.4 MB
3. Review of Linear Classifiers/3. Logistic Regression Review.mp4 41.8 MB
9. Appendix/5. How to Succeed in this Course (Long Version).mp4 41.2 MB
8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.mp4 41.0 MB
1. Welcome/4. Where to get the code and data.mp4 40.9 MB
7. Implementations and Extensions/1. Dual with Slack Variables.mp4 40.8 MB
5. Duality/5. Predictions and Support Vectors.mp4 40.8 MB
4. Linear SVM/6. Slack Variables.mp4 40.6 MB
6. Kernel Methods/2. The Kernel Trick.mp4 39.1 MB
1. Welcome/2. Course Objectives.mp4 39.1 MB
2. Beginner_s Corner/2. Image Classification with SVMs.mp4 38.3 MB
6. Kernel Methods/5. Using the Gaussian Kernel.mp4 37.8 MB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.mp4 35.7 MB
8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.mp4 35.4 MB
6. Kernel Methods/7. Other Kernels.mp4 34.0 MB
1. Welcome/3. Course Outline.mp4 32.8 MB
3. Review of Linear Classifiers/5. Prediction Confidence.mp4 32.1 MB
9. Appendix/9. Python 2 vs Python 3.mp4 31.7 MB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).mp4 31.1 MB
5. Duality/3. Lagrangian Duality (part 2).mp4 30.6 MB
2. Beginner_s Corner/7. How do you get the data How do you process the data.mp4 30.2 MB
6. Kernel Methods/8. Mercer_s Condition.mp4 28.9 MB
7. Implementations and Extensions/7. Support Vector Regression.mp4 28.6 MB
6. Kernel Methods/4. Gaussian Kernel.mp4 28.3 MB
9. Appendix/1. What is the Appendix.mp4 26.7 MB
6. Kernel Methods/3. Polynomial Kernel.mp4 26.6 MB
7. Implementations and Extensions/2. Simple Approaches to Implementation.mp4 25.8 MB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.mp4 23.9 MB
9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.mp4 23.6 MB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).mp4 22.4 MB
5. Duality/4. Relationship to Linear Programming.mp4 21.1 MB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp4 20.8 MB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.mp4 20.2 MB
6. Kernel Methods/1. Kernel Methods Section Introduction.mp4 20.1 MB
7. Implementations and Extensions/8. Multiclass Classification.mp4 20.0 MB
4. Linear SVM/10. Linear SVM Section Summary.mp4 19.9 MB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.mp4 18.5 MB
5. Duality/6. Why Transform Primal to Dual.mp4 17.7 MB
3. Review of Linear Classifiers/4. Loss Function and Regularization.mp4 16.9 MB
1. Welcome/1. Introduction.mp4 16.9 MB
4. Linear SVM/8. Linear SVM with Gradient Descent.mp4 16.4 MB
8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.mp4 16.4 MB
3. Review of Linear Classifiers/2. Normal Vectors.mp4 15.5 MB
5. Duality/1. Duality Section Introduction.mp4 15.4 MB
5. Duality/7. Duality Section Conclusion.mp4 13.9 MB
8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.mp4 13.2 MB
8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.mp4 12.4 MB
6. Kernel Methods/9. Kernel Methods Section Summary.mp4 11.7 MB
FreeCoursesOnline.Me.html 110.9 kB
FTUForum.com.html 102.8 kB
Discuss.FTUForum.com.html 32.7 kB
9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28.3 kB
9. Appendix/11. What order should I take your courses in (part 2).vtt 20.7 kB
9. Appendix/6. How to Code by Yourself (part 1).vtt 19.8 kB
9. Appendix/2. Windows-Focused Environment Setup 2018.vtt 17.8 kB
8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.vtt 17.4 kB
9. Appendix/10. What order should I take your courses in (part 1).vtt 14.5 kB
5. Duality/2. Duality and Lagrangians (part 1).vtt 14.0 kB
4. Linear SVM/5. Linear and Quadratic Programming.vtt 13.5 kB
9. Appendix/5. How to Succeed in this Course (Long Version).vtt 13.1 kB
9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.9 kB
2. Beginner_s Corner/3. Spam Detection with SVMs.vtt 12.7 kB
9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 12.6 kB
4. Linear SVM/4. Linear SVM Objective.vtt 11.9 kB
9. Appendix/7. How to Code by Yourself (part 2).vtt 11.7 kB
3. Review of Linear Classifiers/1. Basic Geometry.vtt 11.7 kB
7. Implementations and Extensions/1. Dual with Slack Variables.vtt 11.5 kB
3. Review of Linear Classifiers/3. Logistic Regression Review.vtt 10.9 kB
7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).vtt 10.8 kB
3. Review of Linear Classifiers/6. Nonlinear Problems.vtt 10.7 kB
5. Duality/5. Predictions and Support Vectors.vtt 9.8 kB
8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.vtt 9.6 kB
4. Linear SVM/3. Margins.vtt 8.8 kB
2. Beginner_s Corner/6. Cross-Validation.vtt 8.5 kB
6. Kernel Methods/2. The Kernel Trick.vtt 8.2 kB
4. Linear SVM/6. Slack Variables.vtt 8.1 kB
3. Review of Linear Classifiers/5. Prediction Confidence.vtt 8.1 kB
7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.vtt 8.0 kB
8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.vtt 8.0 kB
6. Kernel Methods/5. Using the Gaussian Kernel.vtt 7.8 kB
8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.vtt 7.4 kB
6. Kernel Methods/7. Other Kernels.vtt 7.4 kB
1. Welcome/4. Where to get the code and data.vtt 7.1 kB
7. Implementations and Extensions/2. Simple Approaches to Implementation.vtt 7.1 kB
5. Duality/3. Lagrangian Duality (part 2).vtt 6.9 kB
2. Beginner_s Corner/7. How do you get the data How do you process the data.vtt 6.8 kB
1. Welcome/3. Course Outline.vtt 6.8 kB
4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).vtt 6.8 kB
6. Kernel Methods/8. Mercer_s Condition.vtt 6.7 kB
2. Beginner_s Corner/2. Image Classification with SVMs.vtt 6.5 kB
2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.vtt 6.4 kB
2. Beginner_s Corner/4. Medical Diagnosis with SVMs.vtt 6.2 kB
6. Kernel Methods/3. Polynomial Kernel.vtt 6.1 kB
7. Implementations and Extensions/7. Support Vector Regression.vtt 6.0 kB
1. Welcome/2. Course Objectives.vtt 5.9 kB
2. Beginner_s Corner/5. Regression with SVMs.vtt 5.8 kB
9. Appendix/9. Python 2 vs Python 3.vtt 5.5 kB
4. Linear SVM/9. Linear SVM with Gradient Descent (Code).vtt 5.4 kB
6. Kernel Methods/4. Gaussian Kernel.vtt 5.4 kB
4. Linear SVM/2. Linear SVM Problem Setup and Definitions.vtt 5.2 kB
7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).vtt 5.0 kB
7. Implementations and Extensions/8. Multiclass Classification.vtt 5.0 kB
4. Linear SVM/10. Linear SVM Section Summary.vtt 5.0 kB
3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.vtt 4.8 kB
5. Duality/4. Relationship to Linear Programming.vtt 4.7 kB
6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.vtt 4.5 kB
3. Review of Linear Classifiers/4. Loss Function and Regularization.vtt 4.4 kB
5. Duality/1. Duality Section Introduction.vtt 4.3 kB
7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).vtt 4.2 kB
8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.vtt 4.1 kB
6. Kernel Methods/1. Kernel Methods Section Introduction.vtt 4.0 kB
5. Duality/6. Why Transform Primal to Dual.vtt 3.8 kB
4. Linear SVM/1. Linear SVM Section Introduction and Outline.vtt 3.8 kB
3. Review of Linear Classifiers/2. Normal Vectors.vtt 3.7 kB
9. Appendix/1. What is the Appendix.vtt 3.4 kB
4. Linear SVM/8. Linear SVM with Gradient Descent.vtt 3.2 kB
8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.vtt 3.1 kB
5. Duality/7. Duality Section Conclusion.vtt 3.1 kB
9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.vtt 3.0 kB
8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.vtt 3.0 kB
8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.vtt 2.9 kB
6. Kernel Methods/9. Kernel Methods Section Summary.vtt 2.9 kB
1. Welcome/1. Introduction.vtt 2.8 kB
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