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

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文件列表

  1. 9. Appendix/2. Windows-Focused Environment Setup 2018.mp4 194MB
  2. 9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 167MB
  3. 9. Appendix/11. What order should I take your courses in (part 2).mp4 123MB
  4. 9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 118MB
  5. 2. Beginner_s Corner/3. Spam Detection with SVMs.mp4 101MB
  6. 9. Appendix/10. What order should I take your courses in (part 1).mp4 88MB
  7. 7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.mp4 84MB
  8. 9. Appendix/6. How to Code by Yourself (part 1).mp4 83MB
  9. 8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.mp4 80MB
  10. 9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  11. 8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.mp4 72MB
  12. 4. Linear SVM/5. Linear and Quadratic Programming.mp4 64MB
  13. 7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).mp4 59MB
  14. 5. Duality/2. Duality and Lagrangians (part 1).mp4 59MB
  15. 9. Appendix/7. How to Code by Yourself (part 2).mp4 57MB
  16. 2. Beginner_s Corner/6. Cross-Validation.mp4 55MB
  17. 4. Linear SVM/9. Linear SVM with Gradient Descent (Code).mp4 52MB
  18. 2. Beginner_s Corner/5. Regression with SVMs.mp4 51MB
  19. 4. Linear SVM/4. Linear SVM Objective.mp4 49MB
  20. 2. Beginner_s Corner/4. Medical Diagnosis with SVMs.mp4 48MB
  21. 3. Review of Linear Classifiers/6. Nonlinear Problems.mp4 47MB
  22. 3. Review of Linear Classifiers/1. Basic Geometry.mp4 47MB
  23. 8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.mp4 44MB
  24. 4. Linear SVM/3. Margins.mp4 41MB
  25. 7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).mp4 41MB
  26. 3. Review of Linear Classifiers/3. Logistic Regression Review.mp4 40MB
  27. 9. Appendix/5. How to Succeed in this Course (Long Version).mp4 39MB
  28. 8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.mp4 39MB
  29. 1. Welcome/4. Where to get the code and data.mp4 39MB
  30. 7. Implementations and Extensions/1. Dual with Slack Variables.mp4 39MB
  31. 5. Duality/5. Predictions and Support Vectors.mp4 39MB
  32. 4. Linear SVM/6. Slack Variables.mp4 39MB
  33. 6. Kernel Methods/2. The Kernel Trick.mp4 37MB
  34. 1. Welcome/2. Course Objectives.mp4 37MB
  35. 2. Beginner_s Corner/2. Image Classification with SVMs.mp4 36MB
  36. 6. Kernel Methods/5. Using the Gaussian Kernel.mp4 36MB
  37. 2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.mp4 34MB
  38. 8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.mp4 34MB
  39. 6. Kernel Methods/7. Other Kernels.mp4 32MB
  40. 1. Welcome/3. Course Outline.mp4 31MB
  41. 3. Review of Linear Classifiers/5. Prediction Confidence.mp4 31MB
  42. 9. Appendix/9. Python 2 vs Python 3.mp4 30MB
  43. 4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).mp4 30MB
  44. 5. Duality/3. Lagrangian Duality (part 2).mp4 29MB
  45. 2. Beginner_s Corner/7. How do you get the data How do you process the data.mp4 29MB
  46. 6. Kernel Methods/8. Mercer_s Condition.mp4 28MB
  47. 7. Implementations and Extensions/7. Support Vector Regression.mp4 27MB
  48. 6. Kernel Methods/4. Gaussian Kernel.mp4 27MB
  49. 9. Appendix/1. What is the Appendix.mp4 25MB
  50. 6. Kernel Methods/3. Polynomial Kernel.mp4 25MB
  51. 7. Implementations and Extensions/2. Simple Approaches to Implementation.mp4 25MB
  52. 4. Linear SVM/2. Linear SVM Problem Setup and Definitions.mp4 23MB
  53. 9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.mp4 22MB
  54. 7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).mp4 21MB
  55. 5. Duality/4. Relationship to Linear Programming.mp4 20MB
  56. 6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.mp4 20MB
  57. 3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.mp4 19MB
  58. 6. Kernel Methods/1. Kernel Methods Section Introduction.mp4 19MB
  59. 7. Implementations and Extensions/8. Multiclass Classification.mp4 19MB
  60. 4. Linear SVM/10. Linear SVM Section Summary.mp4 19MB
  61. 4. Linear SVM/1. Linear SVM Section Introduction and Outline.mp4 18MB
  62. 5. Duality/6. Why Transform Primal to Dual.mp4 17MB
  63. 3. Review of Linear Classifiers/4. Loss Function and Regularization.mp4 16MB
  64. 1. Welcome/1. Introduction.mp4 16MB
  65. 4. Linear SVM/8. Linear SVM with Gradient Descent.mp4 16MB
  66. 8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.mp4 16MB
  67. 3. Review of Linear Classifiers/2. Normal Vectors.mp4 15MB
  68. 5. Duality/1. Duality Section Introduction.mp4 15MB
  69. 5. Duality/7. Duality Section Conclusion.mp4 13MB
  70. 8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.mp4 13MB
  71. 8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.mp4 12MB
  72. 6. Kernel Methods/9. Kernel Methods Section Summary.mp4 11MB
  73. FreeCoursesOnline.Me.html 108KB
  74. FTUForum.com.html 100KB
  75. Discuss.FTUForum.com.html 32KB
  76. 9. Appendix/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28KB
  77. 9. Appendix/11. What order should I take your courses in (part 2).vtt 20KB
  78. 9. Appendix/6. How to Code by Yourself (part 1).vtt 19KB
  79. 9. Appendix/2. Windows-Focused Environment Setup 2018.vtt 17KB
  80. 8. Neural Networks (Beginner_s Corner 2)/2. RBF Networks.vtt 17KB
  81. 9. Appendix/10. What order should I take your courses in (part 1).vtt 14KB
  82. 5. Duality/2. Duality and Lagrangians (part 1).vtt 14KB
  83. 4. Linear SVM/5. Linear and Quadratic Programming.vtt 13KB
  84. 9. Appendix/5. How to Succeed in this Course (Long Version).vtt 13KB
  85. 9. Appendix/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 13KB
  86. 2. Beginner_s Corner/3. Spam Detection with SVMs.vtt 12KB
  87. 9. Appendix/8. Proof that using Jupyter Notebook is the same as not using it.vtt 12KB
  88. 4. Linear SVM/4. Linear SVM Objective.vtt 12KB
  89. 9. Appendix/7. How to Code by Yourself (part 2).vtt 11KB
  90. 3. Review of Linear Classifiers/1. Basic Geometry.vtt 11KB
  91. 7. Implementations and Extensions/1. Dual with Slack Variables.vtt 11KB
  92. 3. Review of Linear Classifiers/3. Logistic Regression Review.vtt 11KB
  93. 7. Implementations and Extensions/6. SMO (Sequential Minimal Optimization).vtt 11KB
  94. 3. Review of Linear Classifiers/6. Nonlinear Problems.vtt 10KB
  95. 5. Duality/5. Predictions and Support Vectors.vtt 10KB
  96. 8. Neural Networks (Beginner_s Corner 2)/3. RBF Approximations.vtt 9KB
  97. 4. Linear SVM/3. Margins.vtt 9KB
  98. 2. Beginner_s Corner/6. Cross-Validation.vtt 8KB
  99. 6. Kernel Methods/2. The Kernel Trick.vtt 8KB
  100. 4. Linear SVM/6. Slack Variables.vtt 8KB
  101. 3. Review of Linear Classifiers/5. Prediction Confidence.vtt 8KB
  102. 7. Implementations and Extensions/3. SVM with Projected Gradient Descent Code.vtt 8KB
  103. 8. Neural Networks (Beginner_s Corner 2)/6. Relationship to Deep Learning Neural Networks.vtt 8KB
  104. 6. Kernel Methods/5. Using the Gaussian Kernel.vtt 8KB
  105. 8. Neural Networks (Beginner_s Corner 2)/7. Neural Network-SVM Mashup.vtt 7KB
  106. 6. Kernel Methods/7. Other Kernels.vtt 7KB
  107. 1. Welcome/4. Where to get the code and data.vtt 7KB
  108. 7. Implementations and Extensions/2. Simple Approaches to Implementation.vtt 7KB
  109. 5. Duality/3. Lagrangian Duality (part 2).vtt 7KB
  110. 2. Beginner_s Corner/7. How do you get the data How do you process the data.vtt 7KB
  111. 1. Welcome/3. Course Outline.vtt 7KB
  112. 4. Linear SVM/7. Hinge Loss (and its Relationship to Logistic Regression).vtt 7KB
  113. 6. Kernel Methods/8. Mercer_s Condition.vtt 7KB
  114. 2. Beginner_s Corner/2. Image Classification with SVMs.vtt 6KB
  115. 2. Beginner_s Corner/1. Beginner_s Corner Section Introduction.vtt 6KB
  116. 2. Beginner_s Corner/4. Medical Diagnosis with SVMs.vtt 6KB
  117. 6. Kernel Methods/3. Polynomial Kernel.vtt 6KB
  118. 7. Implementations and Extensions/7. Support Vector Regression.vtt 6KB
  119. 1. Welcome/2. Course Objectives.vtt 6KB
  120. 2. Beginner_s Corner/5. Regression with SVMs.vtt 6KB
  121. 9. Appendix/9. Python 2 vs Python 3.vtt 5KB
  122. 4. Linear SVM/9. Linear SVM with Gradient Descent (Code).vtt 5KB
  123. 6. Kernel Methods/4. Gaussian Kernel.vtt 5KB
  124. 4. Linear SVM/2. Linear SVM Problem Setup and Definitions.vtt 5KB
  125. 7. Implementations and Extensions/4. Kernel SVM Gradient Descent with Primal (Theory).vtt 5KB
  126. 7. Implementations and Extensions/8. Multiclass Classification.vtt 5KB
  127. 4. Linear SVM/10. Linear SVM Section Summary.vtt 5KB
  128. 3. Review of Linear Classifiers/7. Linear Classifiers Section Conclusion.vtt 5KB
  129. 5. Duality/4. Relationship to Linear Programming.vtt 5KB
  130. 6. Kernel Methods/6. Why does the Gaussian Kernel correspond to infinite-dimensional features.vtt 4KB
  131. 3. Review of Linear Classifiers/4. Loss Function and Regularization.vtt 4KB
  132. 5. Duality/1. Duality Section Introduction.vtt 4KB
  133. 7. Implementations and Extensions/5. Kernel SVM Gradient Descent with Primal (Code).vtt 4KB
  134. 8. Neural Networks (Beginner_s Corner 2)/5. Build Your Own RBF Network.vtt 4KB
  135. 6. Kernel Methods/1. Kernel Methods Section Introduction.vtt 4KB
  136. 5. Duality/6. Why Transform Primal to Dual.vtt 4KB
  137. 4. Linear SVM/1. Linear SVM Section Introduction and Outline.vtt 4KB
  138. 3. Review of Linear Classifiers/2. Normal Vectors.vtt 4KB
  139. 9. Appendix/1. What is the Appendix.vtt 3KB
  140. 4. Linear SVM/8. Linear SVM with Gradient Descent.vtt 3KB
  141. 8. Neural Networks (Beginner_s Corner 2)/1. Neural Networks Section Introduction.vtt 3KB
  142. 5. Duality/7. Duality Section Conclusion.vtt 3KB
  143. 9. Appendix/12. [Bonus] Where to get discount coupons and FREE deep learning material.vtt 3KB
  144. 8. Neural Networks (Beginner_s Corner 2)/4. What Happened to Infinite Dimensionality.vtt 3KB
  145. 8. Neural Networks (Beginner_s Corner 2)/8. Neural Networks Section Conclusion.vtt 3KB
  146. 6. Kernel Methods/9. Kernel Methods Section Summary.vtt 3KB
  147. 1. Welcome/1. Introduction.vtt 3KB
  148. [TGx]Downloaded from torrentgalaxy.org.txt 524B
  149. How you can help Team-FTU.txt 235B
  150. Torrent Downloaded From GloDls.to.txt 84B