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[] Udemy - Deep Learning Prerequisites Logistic Regression in Python

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文件列表

  1. 8/1. Anaconda Environment Setup.mp4 186MB
  2. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp4 78MB
  3. 1. Start Here/3. Statistics vs. Machine Learning.mp4 56MB
  4. 8/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 44MB
  5. 1. Start Here/2. How to Succeed in this Course.mp4 44MB
  6. 1. Start Here/1. Introduction and Outline.mp4 39MB
  7. 10/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 39MB
  8. 11. Appendix FAQ Finale/2. BONUS.srt 38MB
  9. 11. Appendix FAQ Finale/2. BONUS.mp4 38MB
  10. 10/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 38MB
  11. 10/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 29MB
  12. 2/5. Interpretation of Logistic Regression Output.mp4 28MB
  13. 3. Solving for the optimal weights/7. Maximizing the likelihood.mp4 25MB
  14. 4. Practical concerns/8. The donut problem.mp4 25MB
  15. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).mp4 25MB
  16. 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.mp4 24MB
  17. 4. Practical concerns/10. Why Divide by Square Root of D.mp4 23MB
  18. 7. Background Review/1. Gradient Descent Tutorial.mp4 23MB
  19. 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.mp4 21MB
  20. 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.mp4 17MB
  21. 2/10. Suggestion Box.mp4 16MB
  22. 2/3. How do we calculate the output of a neuron logistic classifier - Theory.mp4 15MB
  23. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code by Yourself (part 2).mp4 15MB
  24. 1. Start Here/5. Introduction to the E-Commerce Course Project.mp4 15MB
  25. 4. Practical concerns/3. L2 Regularization - Theory.mp4 15MB
  26. 4. Practical concerns/9. The XOR problem.mp4 14MB
  27. 6. Project Facial Expression Recognition/4. Utilities walkthrough.mp4 13MB
  28. 10/1. How to Succeed in this Course (Long Version).mp4 13MB
  29. 4. Practical concerns/6. L1 Regularization - Code.mp4 12MB
  30. 5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.mp4 11MB
  31. 2/6. E-Commerce Course Project Pre-Processing the Data.mp4 11MB
  32. 6. Project Facial Expression Recognition/3. The class imbalance problem.mp4 10MB
  33. 6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.mp4 10MB
  34. 2/2. Biological inspiration - the neuron.mp4 9MB
  35. 3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.mp4 9MB
  36. 3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.mp4 9MB
  37. 3. Solving for the optimal weights/5. The cross-entropy error function - Code.mp4 9MB
  38. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Python 2 vs Python 3.mp4 8MB
  39. 2/1. Linear Classification.mp4 8MB
  40. 3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.mp4 7MB
  41. 3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..mp4 6MB
  42. 4. Practical concerns/2. Interpreting the Weights.mp4 6MB
  43. 2/4. How do we calculate the output of a neuron logistic classifier - Code.mp4 6MB
  44. 2/7. E-Commerce Course Project Making Predictions.mp4 6MB
  45. 11. Appendix FAQ Finale/1. What is the Appendix.mp4 5MB
  46. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Uncompress a .tar.gz file.mp4 5MB
  47. 3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.mp4 5MB
  48. 5. Checkpoint and applications How to make sure you know your stuff/2. BONUS Exercises + how to get good at this.mp4 5MB
  49. 4. Practical concerns/7. L1 vs L2 Regularization.mp4 5MB
  50. 4. Practical concerns/1. Practical Section Introduction.mp4 5MB
  51. 3. Solving for the optimal weights/4. The cross-entropy error function - Theory.mp4 5MB
  52. 4. Practical concerns/4. L2 Regularization - Code.mp4 4MB
  53. 4. Practical concerns/5. L1 Regularization - Theory.mp4 4MB
  54. 4. Practical concerns/11. Practical Section Summary.mp4 3MB
  55. 3. Solving for the optimal weights/11. Training Section Summary.mp4 3MB
  56. 1. Start Here/4. Review of the classification problem.mp4 3MB
  57. 6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.mp4 3MB
  58. 3. Solving for the optimal weights/1. Training Section Introduction.mp4 3MB
  59. 2/8. Feedforward Quiz.mp4 2MB
  60. 2/9. Prediction Section Summary.mp4 2MB
  61. 10/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32KB
  62. 10/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23KB
  63. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code by Yourself (part 1).srt 23KB
  64. 8/1. Anaconda Environment Setup.srt 20KB
  65. 10/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16KB
  66. 6. Project Facial Expression Recognition/2. Facial Expression Recognition Problem Description.srt 16KB
  67. 1. Start Here/3. Statistics vs. Machine Learning.srt 15KB
  68. 10/1. How to Succeed in this Course (Long Version).srt 15KB
  69. 8/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14KB
  70. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.srt 14KB
  71. 1. Start Here/5. Introduction to the E-Commerce Course Project.srt 14KB
  72. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code by Yourself (part 2).srt 13KB
  73. 4. Practical concerns/3. L2 Regularization - Theory.srt 12KB
  74. 1. Start Here/1. Introduction and Outline.srt 11KB
  75. 4. Practical concerns/10. Why Divide by Square Root of D.srt 9KB
  76. 1. Start Here/2. How to Succeed in this Course.srt 8KB
  77. 3. Solving for the optimal weights/8. Updating the weights using gradient descent - Theory.srt 8KB
  78. 6. Project Facial Expression Recognition/5. Facial Expression Recognition in Code.srt 8KB
  79. 6. Project Facial Expression Recognition/3. The class imbalance problem.srt 8KB
  80. 4. Practical concerns/8. The donut problem.srt 7KB
  81. 3. Solving for the optimal weights/2. A closed-form solution to the Bayes classifier.srt 7KB
  82. 6. Project Facial Expression Recognition/1. Facial Expression Recognition Project Introduction.srt 6KB
  83. 5. Checkpoint and applications How to make sure you know your stuff/1. BONUS Sentiment Analysis.srt 6KB
  84. 2/5. Interpretation of Logistic Regression Output.srt 6KB
  85. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Python 2 vs Python 3.srt 6KB
  86. 4. Practical concerns/9. The XOR problem.srt 6KB
  87. 6. Project Facial Expression Recognition/4. Utilities walkthrough.srt 6KB
  88. 7. Background Review/1. Gradient Descent Tutorial.srt 6KB
  89. 3. Solving for the optimal weights/10. E-Commerce Course Project Training the Logistic Model.srt 5KB
  90. 3. Solving for the optimal weights/3. What do all these symbols mean X, Y, N, D, L, J, P(Y=1X), etc..srt 5KB
  91. 2/1. Linear Classification.srt 5KB
  92. 2/6. E-Commerce Course Project Pre-Processing the Data.srt 5KB
  93. 4. Practical concerns/2. Interpreting the Weights.srt 5KB
  94. 2/10. Suggestion Box.srt 5KB
  95. 4. Practical concerns/6. L1 Regularization - Code.srt 5KB
  96. 2/4. How do we calculate the output of a neuron logistic classifier - Code.srt 4KB
  97. 3. Solving for the optimal weights/4. The cross-entropy error function - Theory.srt 4KB
  98. 2/2. Biological inspiration - the neuron.srt 4KB
  99. 4. Practical concerns/7. L1 vs L2 Regularization.srt 4KB
  100. 9. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Uncompress a .tar.gz file.srt 4KB
  101. 3. Solving for the optimal weights/7. Maximizing the likelihood.srt 4KB
  102. 3. Solving for the optimal weights/5. The cross-entropy error function - Code.srt 4KB
  103. 2/3. How do we calculate the output of a neuron logistic classifier - Theory.srt 4KB
  104. 5. Checkpoint and applications How to make sure you know your stuff/2. BONUS Exercises + how to get good at this.srt 4KB
  105. 4. Practical concerns/5. L1 Regularization - Theory.srt 4KB
  106. 11. Appendix FAQ Finale/1. What is the Appendix.srt 4KB
  107. 4. Practical concerns/1. Practical Section Introduction.srt 3KB
  108. 2/7. E-Commerce Course Project Making Predictions.srt 3KB
  109. 4. Practical concerns/11. Practical Section Summary.srt 3KB
  110. 3. Solving for the optimal weights/11. Training Section Summary.srt 3KB
  111. 3. Solving for the optimal weights/9. Updating the weights using gradient descent - Code.srt 2KB
  112. 3. Solving for the optimal weights/6. Visualizing the linear discriminant Bayes classifier Gaussian clouds.srt 2KB
  113. 1. Start Here/4. Review of the classification problem.srt 2KB
  114. 3. Solving for the optimal weights/1. Training Section Introduction.srt 2KB
  115. 2/8. Feedforward Quiz.srt 2KB
  116. 4. Practical concerns/4. L2 Regularization - Code.srt 2KB
  117. 6. Project Facial Expression Recognition/6. Facial Expression Recognition Project Summary.srt 2KB
  118. 2/9. Prediction Section Summary.srt 1KB
  119. 1. Start Here/6. Easy first quiz.html 152B
  120. 1. Start Here/[Tutorialsplanet.NET].url 128B
  121. 7. Background Review/[Tutorialsplanet.NET].url 128B
  122. [Tutorialsplanet.NET].url 128B