-
Урок 1. 00:01:48Introduction
-
Урок 2. 00:00:47Finding the codes (Github)
-
Урок 3. 00:02:42A Look at the Projects
-
Урок 4. 00:00:19Intro
-
Урок 5. 00:08:541 Dimensional Tensors
-
Урок 6. 00:05:24Vector Operations
-
Урок 7. 00:05:312 Dimensional Tensors
-
Урок 8. 00:03:04Slicing 3D Tensors
-
Урок 9. 00:03:22Matrix Multiplication
-
Урок 10. 00:04:24Gradient with PyTorch
-
Урок 11. 00:00:14Outro
-
Урок 12. 00:00:45Intro
-
Урок 13. 00:06:16Making Predictions
-
Урок 14. 00:04:30Linear Class
-
Урок 15. 00:08:10Custom Modules
-
Урок 16. 00:10:36Creating Dataset
-
Урок 17. 00:03:34Loss Function
-
Урок 18. 00:04:42Gradient Descent
-
Урок 19. 00:03:16Mean Squared Error
-
Урок 20. 00:11:37Training - Code Implementation
-
Урок 21. 00:00:32Outro
-
Урок 22. 00:00:35Intro
-
Урок 23. 00:01:20What is Deep Learning
-
Урок 24. 00:09:35Creating Dataset
-
Урок 25. 00:11:57Perceptron Model
-
Урок 26. 00:11:23Model Setup
-
Урок 27. 00:10:39Model Training
-
Урок 28. 00:05:24Model Testing
-
Урок 29. 00:00:24Outro
-
Урок 30. 00:00:29Intro
-
Урок 31. 00:03:12Non-Linear Boundaries
-
Урок 32. 00:09:07Architecture
-
Урок 33. 00:07:47Feedforward Process
-
Урок 34. 00:04:11Error Function
-
Урок 35. 00:05:04Backpropagation
-
Урок 36. 00:08:50Code Implementation
-
Урок 37. 00:15:22Testing Model
-
Урок 38. 00:00:23Outro
-
Урок 39. 00:00:37Intro
-
Урок 40. 00:05:51MNIST Dataset
-
Урок 41. 00:12:40Training and Test Datasets
-
Урок 42. 00:16:27Image Transforms
-
Урок 43. 00:30:45Neural Network Implementation
-
Урок 44. 00:12:22Neural Network Validation
-
Урок 45. 00:13:27Final Tests
-
Урок 46. 00:01:29A note on adjusting batch size
-
Урок 47. 00:00:22Outro
-
Урок 48. 00:06:10Convolutions and MNIST
-
Урок 49. 00:18:12Convolutional Layer
-
Урок 50. 00:08:08Convolutions II
-
Урок 51. 00:14:12Pooling
-
Урок 52. 00:06:24Fully Connected Network
-
Урок 53. 00:12:47Neural Network Implementation with PyTorch
-
Урок 54. 00:17:19Model Training with PyTorch
-
Урок 55. 00:01:45The CIFAR 10 Dataset
-
Урок 56. 00:09:52Testing LeNet
-
Урок 57. 00:07:53Hyperparameter Tuning
-
Урок 58. 00:12:26Data Augmentation
-
Урок 59. 00:14:41Pre-trained Sophisticated Models
-
Урок 60. 00:27:35AlexNet and VGG16
-
Урок 61. 00:09:46VGG 19
-
Урок 62. 00:17:27Image Transforms
-
Урок 63. 00:12:10Feature Extraction
-
Урок 64. 00:12:02The Gram Matrix
-
Урок 65. 00:25:13Optimization
-
Урок 66. 00:10:07Style Transfer with Video
-
Урок 67. 00:00:56Python Crash Course - Free Access
-
Урок 68. 00:00:49Overview
-
Урок 69. 00:12:04Arrays vs Lists
-
Урок 70. 00:11:47Multidimensional Arrays
-
Урок 71. 00:03:34One Dimensional Slicing
-
Урок 72. 00:03:35Reshaping
-
Урок 73. 00:07:21Multidimensional Slicing
-
Урок 74. 00:08:18Manipulating Array Shapes
-
Урок 75. 00:04:20Matrix Multiplication
-
Урок 76. 00:13:51Stacking
-
Урок 77. 00:00:09Outro
-
Урок 78. 00:11:47Softmax
-
Урок 79. 00:08:02Cross Entropy
- Категории
- Источники
- Все курсы
- Разделы
- Книги
https://www.udemy.com/course/pytorch-deep-learning/
https://www.udemy.com/course/pytorch-deep-learning/
He is the best teacher on AI/ML on Udemy, real expert with 20+ courses. But this course is the latest and most 'all-in-1'.
https://www.udemy.com/course/pytorch-deep-learning/