• Урок 1. 00:01:48
    Introduction
  • Урок 2. 00:00:47
    Finding the codes (Github)
  • Урок 3. 00:02:42
    A Look at the Projects
  • Урок 4. 00:00:19
    Intro
  • Урок 5. 00:08:54
    1 Dimensional Tensors
  • Урок 6. 00:05:24
    Vector Operations
  • Урок 7. 00:05:31
    2 Dimensional Tensors
  • Урок 8. 00:03:04
    Slicing 3D Tensors
  • Урок 9. 00:03:22
    Matrix Multiplication
  • Урок 10. 00:04:24
    Gradient with PyTorch
  • Урок 11. 00:00:14
    Outro
  • Урок 12. 00:00:45
    Intro
  • Урок 13. 00:06:16
    Making Predictions
  • Урок 14. 00:04:30
    Linear Class
  • Урок 15. 00:08:10
    Custom Modules
  • Урок 16. 00:10:36
    Creating Dataset
  • Урок 17. 00:03:34
    Loss Function
  • Урок 18. 00:04:42
    Gradient Descent
  • Урок 19. 00:03:16
    Mean Squared Error
  • Урок 20. 00:11:37
    Training - Code Implementation
  • Урок 21. 00:00:32
    Outro
  • Урок 22. 00:00:35
    Intro
  • Урок 23. 00:01:20
    What is Deep Learning
  • Урок 24. 00:09:35
    Creating Dataset
  • Урок 25. 00:11:57
    Perceptron Model
  • Урок 26. 00:11:23
    Model Setup
  • Урок 27. 00:10:39
    Model Training
  • Урок 28. 00:05:24
    Model Testing
  • Урок 29. 00:00:24
    Outro
  • Урок 30. 00:00:29
    Intro
  • Урок 31. 00:03:12
    Non-Linear Boundaries
  • Урок 32. 00:09:07
    Architecture
  • Урок 33. 00:07:47
    Feedforward Process
  • Урок 34. 00:04:11
    Error Function
  • Урок 35. 00:05:04
    Backpropagation
  • Урок 36. 00:08:50
    Code Implementation
  • Урок 37. 00:15:22
    Testing Model
  • Урок 38. 00:00:23
    Outro
  • Урок 39. 00:00:37
    Intro
  • Урок 40. 00:05:51
    MNIST Dataset
  • Урок 41. 00:12:40
    Training and Test Datasets
  • Урок 42. 00:16:27
    Image Transforms
  • Урок 43. 00:30:45
    Neural Network Implementation
  • Урок 44. 00:12:22
    Neural Network Validation
  • Урок 45. 00:13:27
    Final Tests
  • Урок 46. 00:01:29
    A note on adjusting batch size
  • Урок 47. 00:00:22
    Outro
  • Урок 48. 00:06:10
    Convolutions and MNIST
  • Урок 49. 00:18:12
    Convolutional Layer
  • Урок 50. 00:08:08
    Convolutions II
  • Урок 51. 00:14:12
    Pooling
  • Урок 52. 00:06:24
    Fully Connected Network
  • Урок 53. 00:12:47
    Neural Network Implementation with PyTorch
  • Урок 54. 00:17:19
    Model Training with PyTorch
  • Урок 55. 00:01:45
    The CIFAR 10 Dataset
  • Урок 56. 00:09:52
    Testing LeNet
  • Урок 57. 00:07:53
    Hyperparameter Tuning
  • Урок 58. 00:12:26
    Data Augmentation
  • Урок 59. 00:14:41
    Pre-trained Sophisticated Models
  • Урок 60. 00:27:35
    AlexNet and VGG16
  • Урок 61. 00:09:46
    VGG 19
  • Урок 62. 00:17:27
    Image Transforms
  • Урок 63. 00:12:10
    Feature Extraction
  • Урок 64. 00:12:02
    The Gram Matrix
  • Урок 65. 00:25:13
    Optimization
  • Урок 66. 00:10:07
    Style Transfer with Video
  • Урок 67. 00:00:56
    Python Crash Course - Free Access
  • Урок 68. 00:00:49
    Overview
  • Урок 69. 00:12:04
    Arrays vs Lists
  • Урок 70. 00:11:47
    Multidimensional Arrays
  • Урок 71. 00:03:34
    One Dimensional Slicing
  • Урок 72. 00:03:35
    Reshaping
  • Урок 73. 00:07:21
    Multidimensional Slicing
  • Урок 74. 00:08:18
    Manipulating Array Shapes
  • Урок 75. 00:04:20
    Matrix Multiplication
  • Урок 76. 00:13:51
    Stacking
  • Урок 77. 00:00:09
    Outro
  • Урок 78. 00:11:47
    Softmax
  • Урок 79. 00:08:02
    Cross Entropy
Этот курс находится в платной подписке. Оформи премиум подписку и смотри PyTorch for Deep Learning and Computer Vision, а также все другие курсы, прямо сейчас!
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