Видео курса

  • Урок 1. 00:06:42
    COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
  • Урок 2. 00:18:22
    Installation and Environment Setup
  • Урок 3. 00:00:45
    Introduction to NumPy
  • Урок 4. 00:10:46
    NumPy Arrays
  • Урок 5. 00:08:11
    NumPy Arrays Part Two
  • Урок 6. 00:11:36
    Numpy Index Selection
  • Урок 7. 00:06:47
    NumPy Operations
  • Урок 8. 00:01:19
    Numpy Exercises
  • Урок 9. 00:07:06
    Numpy Exercises - Solutions
  • Урок 10. 00:01:11
    Pandas Overview
  • Урок 11. 00:10:02
    Pandas Series
  • Урок 12. 00:13:25
    Pandas DataFrames - Part One
  • Урок 13. 00:11:10
    Pandas DataFrames - Part Two
  • Урок 14. 00:05:44
    GroupBy Operations
  • Урок 15. 00:09:22
    Pandas Operations
  • Урок 16. 00:10:19
    Data Input and Output
  • Урок 17. 00:03:39
    Pandas Exercises
  • Урок 18. 00:08:36
    Pandas Exercises - Solutions
  • Урок 19. 00:03:21
    PyTorch Basics Introduction
  • Урок 20. 00:08:11
    Tensor Basics
  • Урок 21. 00:15:13
    Tensor Basics - Part Two
  • Урок 22. 00:13:30
    Tensor Operations
  • Урок 23. 00:06:28
    Tensor Operations - Part Two
  • Урок 24. 00:02:34
    PyTorch Basics - Exercise
  • Урок 25. 00:05:22
    PyTorch Basics - Exercise Solutions
  • Урок 26. 00:03:41
    What is Machine Learning?
  • Урок 27. 00:08:22
    Supervised Learning
  • Урок 28. 00:08:00
    Overfitting
  • Урок 29. 00:16:38
    Evaluating Performance - Classification Error Metrics
  • Урок 30. 00:05:37
    Evaluating Performance - Regression Error Metrics
  • Урок 31. 00:04:45
    Unsupervised Learning
  • Урок 32. 00:01:46
    Introduction to ANN Section
  • Урок 33. 00:10:40
    Theory - Perceptron Model
  • Урок 34. 00:07:20
    Theory - Neural Network
  • Урок 35. 00:10:40
    Theory - Activation Functions
  • Урок 36. 00:10:35
    Multi-Class Classification
  • Урок 37. 00:18:14
    Theory - Cost Functions and Gradient Descent
  • Урок 38. 00:14:48
    Theory - BackPropagation
  • Урок 39. 00:12:24
    PyTorch Gradients
  • Урок 40. 00:11:02
    Linear Regression with PyTorch
  • Урок 41. 00:20:32
    Linear Regression with PyTorch - Part Two
  • Урок 42. 00:16:00
    DataSets with PyTorch
  • Урок 43. 00:11:35
    Basic Pytorch ANN - Part One
  • Урок 44. 00:15:36
    Basic PyTorch ANN - Part Two
  • Урок 45. 00:14:24
    Basic PyTorch ANN - Part Three
  • Урок 46. 00:06:53
    Introduction to Full ANN with PyTorch
  • Урок 47. 00:19:36
    Full ANN Code Along - Regression - Part One - Feature Engineering
  • Урок 48. 00:19:43
    Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features
  • Урок 49. 00:17:10
    Full ANN Code Along - Regression - Part Three - Tabular Model
  • Урок 50. 00:16:43
    Full ANN Code Along - Regression - Part Four - Training and Evaluation
  • Урок 51. 00:06:53
    Full ANN Code Along - Classification Example
  • Урок 52. 00:05:31
    ANN - Exercise Overview
  • Урок 53. 00:16:26
    ANN - Exercise Solutions
  • Урок 54. 00:01:57
    Introduction to CNNs
  • Урок 55. 00:03:26
    Understanding the MNIST data set
  • Урок 56. 00:19:23
    ANN with MNIST - Part One - Data
  • Урок 57. 00:10:35
    ANN with MNIST - Part Two - Creating the Network
  • Урок 58. 00:15:29
    ANN with MNIST - Part Three - Training
  • Урок 59. 00:09:16
    ANN with MNIST - Part Four - Evaluation
  • Урок 60. 00:11:36
    Image Filters and Kernels
  • Урок 61. 00:14:02
    Convolutional Layers
  • Урок 62. 00:06:48
    Pooling Layers
  • Урок 63. 00:02:12
    MNIST Data Revisited
  • Урок 64. 00:18:22
    MNIST with CNN - Code Along - Part One
  • Урок 65. 00:18:19
    MNIST with CNN - Code Along - Part Two
  • Урок 66. 00:08:58
    MNIST with CNN - Code Along - Part Three
  • Урок 67. 00:07:14
    CIFAR-10 DataSet with CNN - Code Along - Part One
  • Урок 68. 00:18:41
    CIFAR-10 DataSet with CNN - Code Along - Part Two
  • Урок 69. 00:16:13
    Loading Real Image Data - Part One
  • Урок 70. 00:18:27
    Loading Real Image Data - Part Two
  • Урок 71. 00:22:21
    CNN on Custom Images - Part One - Loading Data
  • Урок 72. 00:13:10
    CNN on Custom Images - Part Two - Training and Evaluating Model
  • Урок 73. 00:14:15
    CNN on Custom Images - Part Three - PreTrained Networks
  • Урок 74. 00:02:50
    CNN Exercise
  • Урок 75. 00:07:53
    CNN Exercise Solutions
  • Урок 76. 00:02:01
    Introduction to Recurrent Neural Networks
  • Урок 77. 00:07:42
    RNN Basic Theory
  • Урок 78. 00:06:48
    Vanishing Gradients
  • Урок 79. 00:11:24
    LSTMS and GRU
  • Урок 80. 00:07:50
    RNN Batches Theory
  • Урок 81. 00:12:12
    RNN - Creating Batches with Data
  • Урок 82. 00:12:57
    Basic RNN - Creating the LSTM Model
  • Урок 83. 00:20:29
    Basic RNN - Training and Forecasting
  • Урок 84. 00:14:36
    RNN on a Time Series - Part One
  • Урок 85. 00:18:46
    RNN on a Time Series - Part Two
  • Урок 86. 00:04:15
    RNN Exercise
  • Урок 87. 00:11:32
    RNN Exercise - Solutions
  • Урок 88. 00:13:08
    Why do we need GPUs?
  • Урок 89. 00:17:41
    Using GPU for PyTorch
  • Урок 90. 00:02:38
    Introduction to NLP with PyTorch
  • Урок 91. 00:15:50
    Encoding Text Data
  • Урок 92. 00:14:41
    Generating Training Batches
  • Урок 93. 00:12:35
    Creating the LSTM Model
  • Урок 94. 00:11:55
    Training the LSTM Model
  • Урок 95. 00:10:32
    Generating Predictions
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