589689.xyz

[] Udemy - Deep Learning Prerequisites Linear Regression in Python

  • 收录时间:2020-09-16 02:58:10
  • 文件大小:1GB
  • 下载次数:9
  • 最近下载:2020-10-19 08:24:13
  • 磁力链接:
立即下载 复制链接

文件列表

  1. 6. Setting Up Your Environment/1. Windows-Focused Environment Setup 2018.mp4 186MB
  2. 7. Extra Help With Python Coding for Beginners/3. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  3. 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.mp4 60MB
  4. 1. Welcome/5. Statistics vs. Machine Learning.mp4 56MB
  5. 1. Welcome/1. Welcome.mp4 50MB
  6. 6. Setting Up Your Environment/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  7. 8. Effective Learning Strategies for Machine Learning/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  8. 9. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 38MB
  9. 8. Effective Learning Strategies for Machine Learning/4. What order should I take your courses in (part 2).mp4 38MB
  10. 8. Effective Learning Strategies for Machine Learning/3. What order should I take your courses in (part 1).mp4 29MB
  11. 1. Welcome/4. How to Succeed in this Course.mp4 28MB
  12. 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.mp4 25MB
  13. 7. Extra Help With Python Coding for Beginners/1. How to Code by Yourself (part 1).mp4 25MB
  14. 4. Practical machine learning issues/17. Why Divide by Square Root of D.mp4 23MB
  15. 4. Practical machine learning issues/11. Gradient Descent Tutorial.mp4 23MB
  16. 2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.mp4 20MB
  17. 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.mp4 20MB
  18. 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).mp4 19MB
  19. 8. Effective Learning Strategies for Machine Learning/1. How to Succeed in this Course (Long Version).mp4 18MB
  20. 2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.mp4 17MB
  21. 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.mp4 17MB
  22. 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).mp4 16MB
  23. 2. 1-D Linear Regression Theory and Code/11. Suggestion Box.mp4 16MB
  24. 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.mp4 15MB
  25. 7. Extra Help With Python Coding for Beginners/2. How to Code by Yourself (part 2).mp4 15MB
  26. 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.mp4 14MB
  27. 4. Practical machine learning issues/2. Interpreting the Weights.mp4 14MB
  28. 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.mp4 12MB
  29. 4. Practical machine learning issues/1. What do all these letters mean.mp4 10MB
  30. 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.mp4 9MB
  31. 1. Welcome/3. What is machine learning How does linear regression play a role.mp4 8MB
  32. 4. Practical machine learning issues/15. L1 Regularization - Code.mp4 8MB
  33. 4. Practical machine learning issues/5. Categorical inputs.mp4 8MB
  34. 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.mp4 8MB
  35. 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.mp4 8MB
  36. 4. Practical machine learning issues/9. L2 Regularization - Code.mp4 8MB
  37. 7. Extra Help With Python Coding for Beginners/4. Python 2 vs Python 3.mp4 8MB
  38. 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.mp4 7MB
  39. 4. Practical machine learning issues/8. L2 Regularization - Theory.mp4 7MB
  40. 1. Welcome/2. Introduction and Outline.mp4 6MB
  41. 4. Practical machine learning issues/10. The Dummy Variable Trap.mp4 6MB
  42. 9. Appendix FAQ/1. What is the Appendix.mp4 5MB
  43. 4. Practical machine learning issues/16. L1 vs L2 Regularization.mp4 5MB
  44. 4. Practical machine learning issues/14. L1 Regularization - Theory.mp4 5MB
  45. 2. 1-D Linear Regression Theory and Code/6. R-squared in code.mp4 4MB
  46. 2. 1-D Linear Regression Theory and Code/7. Introduction to Moore's Law Problem.mp4 4MB
  47. 4. Practical machine learning issues/3. Generalization error, train and test sets.mp4 4MB
  48. 4. Practical machine learning issues/6. One-Hot Encoding Quiz.mp4 4MB
  49. 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.mp4 4MB
  50. 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.mp4 3MB
  51. 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.mp4 3MB
  52. 2. 1-D Linear Regression Theory and Code/10. R-squared Quiz 1.mp4 3MB
  53. 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.mp4 1MB
  54. 8. Effective Learning Strategies for Machine Learning/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 34KB
  55. 8. Effective Learning Strategies for Machine Learning/4. What order should I take your courses in (part 2).srt 25KB
  56. 7. Extra Help With Python Coding for Beginners/1. How to Code by Yourself (part 1).srt 24KB
  57. 6. Setting Up Your Environment/1. Windows-Focused Environment Setup 2018.srt 22KB
  58. 2. 1-D Linear Regression Theory and Code/1. Define the model in 1-D, derive the solution (Updated Version).srt 18KB
  59. 8. Effective Learning Strategies for Machine Learning/3. What order should I take your courses in (part 1).srt 17KB
  60. 1. Welcome/5. Statistics vs. Machine Learning.srt 16KB
  61. 6. Setting Up Your Environment/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 16KB
  62. 8. Effective Learning Strategies for Machine Learning/1. How to Succeed in this Course (Long Version).srt 15KB
  63. 7. Extra Help With Python Coding for Beginners/3. Proof that using Jupyter Notebook is the same as not using it.srt 15KB
  64. 7. Extra Help With Python Coding for Beginners/2. How to Code by Yourself (part 2).srt 14KB
  65. 3. Multiple linear regression and polynomial regression/2. Define the multi-dimensional problem and derive the solution.srt 13KB
  66. 2. 1-D Linear Regression Theory and Code/2. Define the model in 1-D, derive the solution.srt 11KB
  67. 4. Practical machine learning issues/17. Why Divide by Square Root of D.srt 9KB
  68. 1. Welcome/4. How to Succeed in this Course.srt 9KB
  69. 4. Practical machine learning issues/4. Generalization and Overfitting Demonstration in Code.srt 9KB
  70. 4. Practical machine learning issues/1. What do all these letters mean.srt 9KB
  71. 9. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 8KB
  72. 2. 1-D Linear Regression Theory and Code/9. Moore's Law Derivation.srt 8KB
  73. 2. 1-D Linear Regression Theory and Code/8. Demonstrating Moore's Law in Code.srt 7KB
  74. 4. Practical machine learning issues/7. Probabilistic Interpretation of Squared Error.srt 7KB
  75. 7. Extra Help With Python Coding for Beginners/4. Python 2 vs Python 3.srt 7KB
  76. 4. Practical machine learning issues/8. L2 Regularization - Theory.srt 6KB
  77. 4. Practical machine learning issues/11. Gradient Descent Tutorial.srt 6KB
  78. 4. Practical machine learning issues/10. The Dummy Variable Trap.srt 6KB
  79. 2. 1-D Linear Regression Theory and Code/3. Coding the 1-D solution in Python.srt 6KB
  80. 1. Welcome/2. Introduction and Outline.srt 6KB
  81. 1. Welcome/3. What is machine learning How does linear regression play a role.srt 6KB
  82. 5. Conclusion and Next Steps/1. Brief overview of advanced linear regression and machine learning topics.srt 6KB
  83. 3. Multiple linear regression and polynomial regression/4. Coding the multi-dimensional solution in Python.srt 6KB
  84. 3. Multiple linear regression and polynomial regression/6. Predicting Systolic Blood Pressure from Age and Weight.srt 5KB
  85. 5. Conclusion and Next Steps/2. Exercises, practice, and how to get good at this.srt 5KB
  86. 3. Multiple linear regression and polynomial regression/5. Polynomial regression - extending linear regression (with Python code).srt 5KB
  87. 2. 1-D Linear Regression Theory and Code/11. Suggestion Box.srt 5KB
  88. 4. Practical machine learning issues/5. Categorical inputs.srt 5KB
  89. 1. Welcome/1. Welcome.srt 5KB
  90. 2. 1-D Linear Regression Theory and Code/5. Determine how good the model is - r-squared.srt 5KB
  91. 4. Practical machine learning issues/2. Interpreting the Weights.srt 5KB
  92. 4. Practical machine learning issues/14. L1 Regularization - Theory.srt 5KB
  93. 4. Practical machine learning issues/16. L1 vs L2 Regularization.srt 5KB
  94. 9. Appendix FAQ/1. What is the Appendix.srt 4KB
  95. 4. Practical machine learning issues/13. Bypass the Dummy Variable Trap with Gradient Descent.srt 4KB
  96. 4. Practical machine learning issues/15. L1 Regularization - Code.srt 4KB
  97. 2. 1-D Linear Regression Theory and Code/7. Introduction to Moore's Law Problem.srt 4KB
  98. 4. Practical machine learning issues/9. L2 Regularization - Code.srt 4KB
  99. 4. Practical machine learning issues/12. Gradient Descent for Linear Regression.srt 3KB
  100. 3. Multiple linear regression and polynomial regression/7. R-squared Quiz 2.srt 3KB
  101. 4. Practical machine learning issues/3. Generalization error, train and test sets.srt 3KB
  102. 4. Practical machine learning issues/6. One-Hot Encoding Quiz.srt 3KB
  103. 2. 1-D Linear Regression Theory and Code/10. R-squared Quiz 1.srt 2KB
  104. 3. Multiple linear regression and polynomial regression/3. How to solve multiple linear regression using only matrices.srt 2KB
  105. 2. 1-D Linear Regression Theory and Code/6. R-squared in code.srt 2KB
  106. 2. 1-D Linear Regression Theory and Code/4. Exercise Theory vs. Code.srt 2KB
  107. 1. Welcome/6. What can linear regression be used for.html 150B
  108. [Tutorialsplanet.NET].url 128B