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

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视频 2019-6-21 09:46 2024-5-14 19:34 184 3.05 GB 72
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FTUForum com UDEMY Machine Learning and AI Support Vector Machines in Python FTU

文件列表
  1. 1. Welcome/1. Introduction.mp416.15MB
  2. 1. Welcome/2. Course Objectives.mp437.24MB
  3. 1. Welcome/3. Course Outline.mp431.3MB
  4. 1. Welcome/4. Where to get the code and data.mp439.03MB
  5. 2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.mp434.01MB
  6. 2. Beginner_s Corner/2. Image Classification with SVMs.mp436.49MB
  7. 2. Beginner_s Corner/3. Spam Detection with SVMs.mp4101.47MB
  8. 2. Beginner_s Corner/4. Medical Diagnosis with SVMs.mp447.91MB
  9. 2. Beginner_s Corner/5. Regression with SVMs.mp450.9MB
  10. 2. Beginner_s Corner/6. Cross-Validation.mp454.63MB
  11. 2. Beginner_s Corner/7. How do you get the data How do you process the data.mp428.83MB
  12. 3. Review of Linear Classifiers/1. Basic Geometry.mp446.61MB
  13. 3. Review of Linear Classifiers/2. Normal Vectors.mp414.8MB
  14. 3. Review of Linear Classifiers/3. Logistic Regression Review.mp439.9MB
  15. 3. Review of Linear Classifiers/4. Loss Function and Regularization.mp416.15MB
  16. 3. Review of Linear Classifiers/5. Prediction Confidence.mp430.65MB
  17. 3. Review of Linear Classifiers/6. Nonlinear Problems.mp447.05MB
  18. 3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.mp419.29MB
  19. 4. Linear SVM/10. Linear SVM Section Summary.mp418.99MB
  20. 4. Linear SVM/1. Linear SVM Section Introduction and Outline.mp417.68MB
  21. 4. Linear SVM/2. Linear SVM Problem Setup and Definitions.mp422.84MB
  22. 4. Linear SVM/3. Margins.mp441.49MB
  23. 4. Linear SVM/4. Linear SVM Objective.mp449.17MB
  24. 4. Linear SVM/5. Linear and Quadratic Programming.mp464.22MB
  25. 4. Linear SVM/6. Slack Variables.mp438.68MB
  26. 4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).mp429.69MB
  27. 4. Linear SVM/8. Linear SVM with Gradient Descent.mp415.68MB
  28. 4. Linear SVM/9. Linear SVM with Gradient Descent (Code).mp451.93MB
  29. 5. Duality/1. Duality Section Introduction.mp414.72MB
  30. 5. Duality/2. Duality and Lagrangians (part 1).mp458.69MB
  31. 5. Duality/3. Lagrangian Duality (part 2).mp429.19MB
  32. 5. Duality/4. Relationship to Linear Programming.mp420.12MB
  33. 5. Duality/5. Predictions and Support Vectors.mp438.88MB
  34. 5. Duality/6. Why Transform Primal to Dual.mp416.93MB
  35. 5. Duality/7. Duality Section Conclusion.mp413.22MB
  36. 6. Kernel Methods/1. Kernel Methods Section Introduction.mp419.13MB
  37. 6. Kernel Methods/2. The Kernel Trick.mp437.25MB
  38. 6. Kernel Methods/3. Polynomial Kernel.mp425.37MB
  39. 6. Kernel Methods/4. Gaussian Kernel.mp426.96MB
  40. 6. Kernel Methods/5. Using the Gaussian Kernel.mp436.01MB
  41. 6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp419.85MB
  42. 6. Kernel Methods/7. Other Kernels.mp432.44MB
  43. 6. Kernel Methods/8. Mercer_s Condition.mp427.57MB
  44. 6. Kernel Methods/9. Kernel Methods Section Summary.mp411.14MB
  45. 7. Implementations and Extensions/1. Dual with Slack Variables.mp438.93MB
  46. 7. Implementations and Extensions/2. Simple Approaches to Implementation.mp424.65MB
  47. 7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.mp483.6MB
  48. 7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).mp421.35MB
  49. 7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).mp458.72MB
  50. 7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).mp441.42MB
  51. 7. Implementations and Extensions/7. Support Vector Regression.mp427.24MB
  52. 7. Implementations and Extensions/8. Multiclass Classification.mp419.08MB
  53. 8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.mp415.61MB
  54. 8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.mp479.54MB
  55. 8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.mp444.41MB
  56. 8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.mp412.57MB
  57. 8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.mp439.11MB
  58. 8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.mp433.75MB
  59. 8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.mp472.29MB
  60. 8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.mp411.83MB
  61. 9. Appendix/10. What order should I take your courses in (part 1).mp488.41MB
  62. 9. Appendix/11. What order should I take your courses in (part 2).mp4123MB
  63. 9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.mp422.49MB
  64. 9. Appendix/1. What is the Appendix.mp425.44MB
  65. 9. Appendix/2. Windows-Focused Environment Setup 2018.mp4194.35MB
  66. 9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4167.01MB
  67. 9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4117.69MB
  68. 9. Appendix/5. How to Succeed in this Course (Long Version).mp439.25MB
  69. 9. Appendix/6. How to Code by Yourself (part 1).mp482.57MB
  70. 9. Appendix/7. How to Code by Yourself (part 2).mp456.69MB
  71. 9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp478.29MB
  72. 9. Appendix/9. Python 2 vs Python 3.mp430.25MB
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