• Урок 1. 00:04:30
    Introduction and Outline
  • Урок 2. 00:08:49
    Who should take this course in 2020 and beyond?
  • Урок 3. 00:05:02
    Where to get the code
  • Урок 4. 00:11:56
    Anyone Can Succeed in this Course
  • Урок 5. 00:01:59
    Review Section Introduction
  • Урок 6. 00:05:29
    What does machine learning do?
  • Урок 7. 00:05:01
    Neuron Predictions
  • Урок 8. 00:08:48
    Neuron Training
  • Урок 9. 00:05:34
    Deep Learning Readiness Test
  • Урок 10. 00:03:53
    Review Section Summary
  • Урок 11. 00:04:21
    Neural Networks with No Math
  • Урок 12. 00:08:54
    Introduction to the E-Commerce Course Project
  • Урок 13. 00:05:40
    Prediction: Section Introduction and Outline
  • Урок 14. 00:05:13
    From Logistic Regression to Neural Networks
  • Урок 15. 00:08:07
    Interpreting the Weights of a Neural Network
  • Урок 16. 00:02:55
    Softmax
  • Урок 17. 00:01:31
    Sigmoid vs. Softmax
  • Урок 18. 00:19:43
    Feedforward in Slow-Mo (part 1)
  • Урок 19. 00:10:56
    Feedforward in Slow-Mo (part 2)
  • Урок 20. 00:01:31
    Where to get the code for this course
  • Урок 21. 00:03:40
    Softmax in Code
  • Урок 22. 00:06:24
    Building an entire feedforward neural network in Python
  • Урок 23. 00:05:25
    E-Commerce Course Project: Pre-Processing the Data
  • Урок 24. 00:03:56
    E-Commerce Course Project: Making Predictions
  • Урок 25. 00:03:26
    Prediction Quizzes
  • Урок 26. 00:01:46
    Prediction: Section Summary
  • Урок 27. 00:03:04
    Suggestion Box
  • Урок 28. 00:02:51
    Training: Section Introduction and Outline
  • Урок 29. 00:09:46
    What do all these symbols and letters mean?
  • Урок 30. 00:06:46
    What does it mean to "train" a neural network?
  • Урок 31. 00:07:39
    How to Brace Yourself to Learn Backpropagation
  • Урок 32. 00:11:02
    Categorical Cross-Entropy Loss Function
  • Урок 33. 00:14:42
    Training Logistic Regression with Softmax (part 1)
  • Урок 34. 00:05:42
    Training Logistic Regression with Softmax (part 2)
  • Урок 35. 00:05:14
    Backpropagation (part 1)
  • Урок 36. 00:10:51
    Backpropagation (part 2)
  • Урок 37. 00:17:08
    Backpropagation in code
  • Урок 38. 00:16:13
    Backpropagation (part 3)
  • Урок 39. 00:03:54
    The WRONG Way to Learn Backpropagation
  • Урок 40. 00:08:12
    E-Commerce Course Project: Training Logistic Regression with Softmax
  • Урок 41. 00:06:20
    E-Commerce Course Project: Training a Neural Network
  • Урок 42. 00:05:32
    Training Quiz
  • Урок 43. 00:02:42
    Training: Section Summary
  • Урок 44. 00:01:44
    Practical Issues: Section Introduction and Outline
  • Урок 45. 00:01:07
    Donut and XOR Review
  • Урок 46. 00:04:22
    Donut and XOR Revisited
  • Урок 47. 00:11:39
    Neural Networks for Regression
  • Урок 48. 00:01:27
    Common nonlinearities and their derivatives
  • Урок 49. 00:07:47
    Practical Considerations for Choosing Activation Functions
  • Урок 50. 00:04:12
    Hyperparameters and Cross-Validation
  • Урок 51. 00:04:09
    Manually Choosing Learning Rate and Regularization Penalty
  • Урок 52. 00:06:33
    Why Divide by Square Root of D?
  • Урок 53. 00:06:11
    Practical Issues: Section Summary
  • Урок 54. 00:19:19
    TensorFlow plug-and-play example
  • Урок 55. 00:11:36
    Visualizing what a neural network has learned using TensorFlow Playground
  • Урок 56. 00:03:43
    Where to go from here
  • Урок 57. 00:04:53
    You know more than you think you know
  • Урок 58. 00:05:08
    How to get good at deep learning + exercises
  • Урок 59. 00:08:50
    Deep neural networks in just 3 lines of code with Sci-Kit Learn
  • Урок 60. 00:04:52
    Facial Expression Recognition Project Introduction
  • Урок 61. 00:12:22
    Facial Expression Recognition Problem Description
  • Урок 62. 00:06:02
    The class imbalance problem
  • Урок 63. 00:05:46
    Utilities walkthrough
  • Урок 64. 00:12:15
    Facial Expression Recognition in Code (Binary / Sigmoid)
  • Урок 65. 00:08:58
    Facial Expression Recognition in Code (Logistic Regression Softmax)
  • Урок 66. 00:10:46
    Facial Expression Recognition in Code (ANN Softmax)
  • Урок 67. 00:01:21
    Facial Expression Recognition Project Summary
  • Урок 68. 00:01:04
    Backpropagation Supplementary Lectures Introduction
  • Урок 69. 00:08:55
    Why Learn the Ins and Outs of Backpropagation?
  • Урок 70. 00:04:31
    Gradient Descent Tutorial
  • Урок 71. 00:04:11
    Help with Softmax Derivative
  • Урок 72. 00:11:56
    Backpropagation with Softmax Troubleshooting
  • Урок 73. 00:07:59
    What's the difference between "neural networks" and "deep learning"?
  • Урок 74. 00:11:19
    Who should learn backpropagation in 2020 and beyond?
  • Урок 75. 00:10:44
    Where does this course fit into your deep learning studies?
  • Урок 76. 00:20:21
    Windows-Focused Environment Setup 2018
  • Урок 77. 00:17:33
    How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
  • Урок 78. 00:03:19
    How to Uncompress a .tar.gz file
  • Урок 79. 00:15:55
    How to Code by Yourself (part 1)
  • Урок 80. 00:09:24
    How to Code by Yourself (part 2)
  • Урок 81. 00:12:30
    Proof that using Jupyter Notebook is the same as not using it
  • Урок 82. 00:04:39
    Python 2 vs Python 3
  • Урок 83. 00:10:25
    How to Succeed in this Course (Long Version)
  • Урок 84. 00:22:05
    Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
  • Урок 85. 00:04:58
    Where does this course fit into your deep learning studies? (Old Version)
  • Урок 86. 00:11:20
    Machine Learning and AI Prerequisite Roadmap (pt 1)
  • Урок 87. 00:16:08
    Machine Learning and AI Prerequisite Roadmap (pt 2)
  • Урок 88. 00:02:49
    What is the Appendix?
  • Урок 89. 00:05:32
    BONUS: Where to get Udemy coupons and FREE deep learning material
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