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[] [UDEMY] Deep Learning Prerequisites Linear Regression in Python [FTU]

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文件列表

  1. 6. Appendix/3. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 6. Appendix/11. What order should I take your courses in (part 2).mp4 82MB
  3. 6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  4. 6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  5. 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  6. 6. Appendix/11. What order should I take your courses in (part 2).vtt 38MB
  7. 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 36MB
  8. 1. Welcome/1. Welcome.mp4 32MB
  9. 6. Appendix/10. What order should I take your courses in (part 1).mp4 29MB
  10. 1. Welcome/2. Introduction and Outline.mp4 28MB
  11. 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25MB
  12. 6. Appendix/5. How to Code by Yourself (part 1).mp4 25MB
  13. 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23MB
  14. 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 19MB
  15. 6. Appendix/7. How to Succeed in this Course (Long Version).mp4 18MB
  16. 2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.mp4 17MB
  17. 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 17MB
  18. 6. Appendix/12. Python 2 vs Python 3.mp4 17MB
  19. 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4 16MB
  20. 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 15MB
  21. 6. Appendix/6. How to Code by Yourself (part 2).mp4 15MB
  22. 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp4 14MB
  23. 3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).mp4 14MB
  24. 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4 12MB
  25. 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 11MB
  26. 4. Practical machine learning issues/1. What do all these letters mean.mp4 10MB
  27. 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp4 9MB
  28. 1. Welcome/3. What is machine learning How does linear regression play a role.mp4 8MB
  29. 4. Practical machine learning issues/15. L1 Regularization - Code.mp4 8MB
  30. 4. Practical machine learning issues/5. Categorical inputs.mp4 8MB
  31. 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp4 8MB
  32. 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp4 8MB
  33. 4. Practical machine learning issues/9. L2 Regularization - Code.mp4 8MB
  34. 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp4 7MB
  35. 4. Practical machine learning issues/8. L2 Regularization - Theory.mp4 7MB
  36. 4. Practical machine learning issues/10. The Dummy Variable Trap.mp4 6MB
  37. 4. Practical machine learning issues/2. Interpreting the Weights.mp4 6MB
  38. 6. Appendix/1. What is the Appendix.mp4 5MB
  39. 4. Practical machine learning issues/16. L1 vs L2 Regularization.mp4 5MB
  40. 4. Practical machine learning issues/14. L1 Regularization - Theory.mp4 5MB
  41. 2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp4 4MB
  42. 1. Welcome/4. Introduction to Moore's Law Problem.mp4 4MB
  43. 4. Practical machine learning issues/3. Generalization error, train and test sets.mp4 4MB
  44. 6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 4MB
  45. 4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp4 4MB
  46. 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp4 4MB
  47. 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp4 3MB
  48. 1. Welcome/6. How to Succeed in this Course.mp4 3MB
  49. 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4 3MB
  50. 2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.mp4 3MB
  51. 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp4 1MB
  52. 6. Appendix/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 28KB
  53. 6. Appendix/5. How to Code by Yourself (part 1).vtt 20KB
  54. 6. Appendix/3. Windows-Focused Environment Setup 2018.vtt 17KB
  55. 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).vtt 14KB
  56. 6. Appendix/10. What order should I take your courses in (part 1).vtt 14KB
  57. 6. Appendix/7. How to Succeed in this Course (Long Version).vtt 13KB
  58. 6. Appendix/4. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12KB
  59. 6. Appendix/9. Proof that using Jupyter Notebook is the same as not using it.vtt 12KB
  60. 6. Appendix/6. How to Code by Yourself (part 2).vtt 12KB
  61. 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.vtt 11KB
  62. 3. Multiple linear regression and polynomial regression/1. Define the multi-dimensional problem and derive the solution (Updated Version).vtt 10KB
  63. 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.vtt 10KB
  64. 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.vtt 8KB
  65. 4. Practical machine learning issues/1. What do all these letters mean.vtt 7KB
  66. 2. 1-D Linear Regression Theory and Code/7. Demonstrating Moore's Law in Code.vtt 6KB
  67. 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.vtt 6KB
  68. 6. Appendix/12. Python 2 vs Python 3.vtt 5KB
  69. 1. Welcome/2. Introduction and Outline.vtt 5KB
  70. 1. Welcome/3. What is machine learning How does linear regression play a role.vtt 5KB
  71. 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.vtt 5KB
  72. 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.vtt 5KB
  73. 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.vtt 5KB
  74. 4. Practical machine learning issues/10. The Dummy Variable Trap.vtt 5KB
  75. 4. Practical machine learning issues/8. L2 Regularization - Theory.vtt 5KB
  76. 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.vtt 5KB
  77. 4. Practical machine learning issues/11. Gradient Descent Tutorial.vtt 5KB
  78. 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.vtt 5KB
  79. 4. Practical machine learning issues/5. Categorical inputs.vtt 4KB
  80. 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).vtt 4KB
  81. 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.vtt 4KB
  82. 1. Welcome/1. Welcome.vtt 4KB
  83. 4. Practical machine learning issues/16. L1 vs L2 Regularization.vtt 4KB
  84. 4. Practical machine learning issues/2. Interpreting the Weights.vtt 4KB
  85. 4. Practical machine learning issues/14. L1 Regularization - Theory.vtt 4KB
  86. 1. Welcome/6. How to Succeed in this Course.vtt 3KB
  87. 1. Welcome/4. Introduction to Moore's Law Problem.vtt 3KB
  88. 6. Appendix/1. What is the Appendix.vtt 3KB
  89. 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.vtt 3KB
  90. 4. Practical machine learning issues/15. L1 Regularization - Code.vtt 3KB
  91. 6. Appendix/2. BONUS Where to get Udemy coupons and FREE deep learning material.vtt 3KB
  92. 4. Practical machine learning issues/9. L2 Regularization - Code.vtt 3KB
  93. 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.vtt 3KB
  94. 4. Practical machine learning issues/3. Generalization error, train and test sets.vtt 3KB
  95. 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.vtt 2KB
  96. 4. Practical machine learning issues/6. One-Hot Encoding Quiz.vtt 2KB
  97. 2. 1-D Linear Regression Theory and Code/8. R-squared Quiz 1.vtt 2KB
  98. 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.vtt 2KB
  99. 2. 1-D Linear Regression Theory and Code/6. R-squared in code.vtt 2KB
  100. 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.vtt 1KB
  101. 0. Websites you may like/1. (FreeTutorials.Us) Download Udemy Paid Courses For Free.url 328B
  102. 0. Websites you may like/5. (Discuss.FTUForum.com) FTU Discussion Forum.url 294B
  103. 0. Websites you may like/2. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url 286B
  104. 0. Websites you may like/4. (FTUApps.com) Download Cracked Developers Applications For Free.url 239B
  105. 0. Websites you may like/How you can help Team-FTU.txt 237B
  106. 0. Websites you may like/3. (NulledPremium.com) Download Cracked Website Themes, Plugins, Scripts And Stock Images.url 163B
  107. 1. Welcome/5. What can linear regression be used for.html 143B