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

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  3. Creat Time：2024-06-13
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  5. Last Active：2024-11-13
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    [FTUForum.com] [UDEMY] Machine Learning and AI Support Vector Machines in Python [FTU]

  1. 9. Appendix/2. Windows-Focused Environment Setup 2018.mp4 199.01 MB
  2. 9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 171.02 MB
  3. 9. Appendix/11. What order should I take your courses in (part 2).mp4 125.95 MB
  4. 9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 120.51 MB
  5. 2. Beginner_s Corner/3. Spam Detection with SVMs.mp4 103.90 MB
  6. 9. Appendix/10. What order should I take your courses in (part 1).mp4 90.53 MB
  7. 7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.mp4 85.61 MB
  8. 9. Appendix/6. How to Code by Yourself (part 1).mp4 84.55 MB
  9. 8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.mp4 81.45 MB
  10. 9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 80.17 MB
  11. 8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.mp4 74.03 MB
  12. 4. Linear SVM/5. Linear and Quadratic Programming.mp4 65.76 MB
  13. 7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).mp4 60.13 MB
  14. 5. Duality/2. Duality and Lagrangians (part 1).mp4 60.10 MB
  15. 9. Appendix/7. How to Code by Yourself (part 2).mp4 58.05 MB
  16. 2. Beginner_s Corner/6. Cross-Validation.mp4 55.94 MB
  17. 4. Linear SVM/9. Linear SVM with Gradient Descent (Code).mp4 53.18 MB
  18. 2. Beginner_s Corner/5. Regression with SVMs.mp4 52.12 MB
  19. 4. Linear SVM/4. Linear SVM Objective.mp4 50.35 MB
  20. 2. Beginner_s Corner/4. Medical Diagnosis with SVMs.mp4 49.05 MB
  21. 3. Review of Linear Classifiers/6. Nonlinear Problems.mp4 48.18 MB
  22. 3. Review of Linear Classifiers/1. Basic Geometry.mp4 47.73 MB
  23. 8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.mp4 45.47 MB
  24. 4. Linear SVM/3. Margins.mp4 42.49 MB
  25. 7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).mp4 42.41 MB
  26. 3. Review of Linear Classifiers/3. Logistic Regression Review.mp4 40.85 MB
  27. 9. Appendix/5. How to Succeed in this Course (Long Version).mp4 40.19 MB
  28. 8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.mp4 40.04 MB
  29. 1. Welcome/4. Where to get the code and data.mp4 39.96 MB
  30. 7. Implementations and Extensions/1. Dual with Slack Variables.mp4 39.86 MB
  31. 5. Duality/5. Predictions and Support Vectors.mp4 39.81 MB
  32. 4. Linear SVM/6. Slack Variables.mp4 39.61 MB
  33. 6. Kernel Methods/2. The Kernel Trick.mp4 38.14 MB
  34. 1. Welcome/2. Course Objectives.mp4 38.14 MB
  35. 2. Beginner_s Corner/2. Image Classification with SVMs.mp4 37.37 MB
  36. 6. Kernel Methods/5. Using the Gaussian Kernel.mp4 36.87 MB
  37. 2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.mp4 34.83 MB
  38. 8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.mp4 34.56 MB
  39. 6. Kernel Methods/7. Other Kernels.mp4 33.22 MB
  40. 1. Welcome/3. Course Outline.mp4 32.05 MB
  41. 3. Review of Linear Classifiers/5. Prediction Confidence.mp4 31.38 MB
  42. 9. Appendix/9. Python 2 vs Python 3.mp4 30.98 MB
  43. 4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).mp4 30.40 MB
  44. 5. Duality/3. Lagrangian Duality (part 2).mp4 29.89 MB
  45. 2. Beginner_s Corner/7. How do you get the data How do you process the data.mp4 29.53 MB
  46. 6. Kernel Methods/8. Mercer_s Condition.mp4 28.23 MB
  47. 7. Implementations and Extensions/7. Support Vector Regression.mp4 27.90 MB
  48. 6. Kernel Methods/4. Gaussian Kernel.mp4 27.61 MB
  49. 9. Appendix/1. What is the Appendix.mp4 26.05 MB
  50. 6. Kernel Methods/3. Polynomial Kernel.mp4 25.98 MB
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