1. Урок 1.00:06:42
    COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
  2. Урок 2.00:18:22
    Installation and Environment Setup
  3. Урок 3.00:00:45
    Introduction to NumPy
  4. Урок 4.00:10:46
    NumPy Arrays
  5. Урок 5.00:08:11
    NumPy Arrays Part Two
  6. Урок 6.00:11:36
    Numpy Index Selection
  7. Урок 7.00:06:47
    NumPy Operations
  8. Урок 8.00:01:19
    Numpy Exercises
  9. Урок 9.00:07:06
    Numpy Exercises - Solutions
  10. Урок 10.00:01:11
    Pandas Overview
  11. Урок 11.00:10:02
    Pandas Series
  12. Урок 12.00:13:25
    Pandas DataFrames - Part One
  13. Урок 13.00:11:10
    Pandas DataFrames - Part Two
  14. Урок 14.00:05:44
    GroupBy Operations
  15. Урок 15.00:09:22
    Pandas Operations
  16. Урок 16.00:10:19
    Data Input and Output
  17. Урок 17.00:03:39
    Pandas Exercises
  18. Урок 18.00:08:36
    Pandas Exercises - Solutions
  19. Урок 19.00:03:21
    PyTorch Basics Introduction
  20. Урок 20.00:08:11
    Tensor Basics
  21. Урок 21.00:15:13
    Tensor Basics - Part Two
  22. Урок 22.00:13:30
    Tensor Operations
  23. Урок 23.00:06:28
    Tensor Operations - Part Two
  24. Урок 24.00:02:34
    PyTorch Basics - Exercise
  25. Урок 25.00:05:22
    PyTorch Basics - Exercise Solutions
  26. Урок 26.00:03:41
    What is Machine Learning?
  27. Урок 27.00:08:22
    Supervised Learning
  28. Урок 28.00:08:00
    Overfitting
  29. Урок 29.00:16:38
    Evaluating Performance - Classification Error Metrics
  30. Урок 30.00:05:37
    Evaluating Performance - Regression Error Metrics
  31. Урок 31.00:04:45
    Unsupervised Learning
  32. Урок 32.00:01:46
    Introduction to ANN Section
  33. Урок 33.00:10:40
    Theory - Perceptron Model
  34. Урок 34.00:07:20
    Theory - Neural Network
  35. Урок 35.00:10:40
    Theory - Activation Functions
  36. Урок 36.00:10:35
    Multi-Class Classification
  37. Урок 37.00:18:14
    Theory - Cost Functions and Gradient Descent
  38. Урок 38.00:14:48
    Theory - BackPropagation
  39. Урок 39.00:12:24
    PyTorch Gradients
  40. Урок 40.00:11:02
    Linear Regression with PyTorch
  41. Урок 41.00:20:32
    Linear Regression with PyTorch - Part Two
  42. Урок 42.00:16:00
    DataSets with PyTorch
  43. Урок 43.00:11:35
    Basic Pytorch ANN - Part One
  44. Урок 44.00:15:36
    Basic PyTorch ANN - Part Two
  45. Урок 45.00:14:24
    Basic PyTorch ANN - Part Three
  46. Урок 46.00:06:53
    Introduction to Full ANN with PyTorch
  47. Урок 47.00:19:36
    Full ANN Code Along - Regression - Part One - Feature Engineering
  48. Урок 48.00:19:43
    Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features
  49. Урок 49.00:17:10
    Full ANN Code Along - Regression - Part Three - Tabular Model
  50. Урок 50.00:16:43
    Full ANN Code Along - Regression - Part Four - Training and Evaluation
  51. Урок 51.00:06:53
    Full ANN Code Along - Classification Example
  52. Урок 52.00:05:31
    ANN - Exercise Overview
  53. Урок 53.00:16:26
    ANN - Exercise Solutions
  54. Урок 54.00:01:57
    Introduction to CNNs
  55. Урок 55.00:03:26
    Understanding the MNIST data set
  56. Урок 56.00:19:23
    ANN with MNIST - Part One - Data
  57. Урок 57.00:10:35
    ANN with MNIST - Part Two - Creating the Network
  58. Урок 58.00:15:29
    ANN with MNIST - Part Three - Training
  59. Урок 59.00:09:16
    ANN with MNIST - Part Four - Evaluation
  60. Урок 60.00:11:36
    Image Filters and Kernels
  61. Урок 61.00:14:02
    Convolutional Layers
  62. Урок 62.00:06:48
    Pooling Layers
  63. Урок 63.00:02:12
    MNIST Data Revisited
  64. Урок 64.00:18:22
    MNIST with CNN - Code Along - Part One
  65. Урок 65.00:18:19
    MNIST with CNN - Code Along - Part Two
  66. Урок 66.00:08:58
    MNIST with CNN - Code Along - Part Three
  67. Урок 67.00:07:14
    CIFAR-10 DataSet with CNN - Code Along - Part One
  68. Урок 68.00:18:41
    CIFAR-10 DataSet with CNN - Code Along - Part Two
  69. Урок 69.00:16:13
    Loading Real Image Data - Part One
  70. Урок 70.00:18:27
    Loading Real Image Data - Part Two
  71. Урок 71.00:22:21
    CNN on Custom Images - Part One - Loading Data
  72. Урок 72.00:13:10
    CNN on Custom Images - Part Two - Training and Evaluating Model
  73. Урок 73.00:14:15
    CNN on Custom Images - Part Three - PreTrained Networks
  74. Урок 74.00:02:50
    CNN Exercise
  75. Урок 75.00:07:53
    CNN Exercise Solutions
  76. Урок 76.00:02:01
    Introduction to Recurrent Neural Networks
  77. Урок 77.00:07:42
    RNN Basic Theory
  78. Урок 78.00:06:48
    Vanishing Gradients
  79. Урок 79.00:11:24
    LSTMS and GRU
  80. Урок 80.00:07:50
    RNN Batches Theory
  81. Урок 81.00:12:12
    RNN - Creating Batches with Data
  82. Урок 82.00:12:57
    Basic RNN - Creating the LSTM Model
  83. Урок 83.00:20:29
    Basic RNN - Training and Forecasting
  84. Урок 84.00:14:36
    RNN on a Time Series - Part One
  85. Урок 85.00:18:46
    RNN on a Time Series - Part Two
  86. Урок 86.00:04:15
    RNN Exercise
  87. Урок 87.00:11:32
    RNN Exercise - Solutions
  88. Урок 88.00:13:08
    Why do we need GPUs?
  89. Урок 89.00:17:41
    Using GPU for PyTorch
  90. Урок 90.00:02:38
    Introduction to NLP with PyTorch
  91. Урок 91.00:15:50
    Encoding Text Data
  92. Урок 92.00:14:41
    Generating Training Batches
  93. Урок 93.00:12:35
    Creating the LSTM Model
  94. Урок 94.00:11:55
    Training the LSTM Model
  95. Урок 95.00:10:32
    Generating Predictions