【双语字幕】伯克利CS 182《深度学习:深度神经网络设计、可视化与理解》课程(2021) by Sergey Levine

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1. CS 182- Lecture 1, Part 1- Introduction
15:56
2. CS 182- Lecture 1, Part 2- Introduction
27:21
3. CS 182- Lecture 1, Part 3- Introduction
25:47
4. CS 182- Lecture 2, Part 1- Machine Learning Basics
16:26
5. CS 182- Lecture 2, Part 2- Machine Learning Basics
27:01
6. CS 182- Lecture 2, Part 3- Machine Learning Basics
20:39
7. CS 182- Lecture 2, Part 4- Machine Learning Basics
20:36
8. CS 182 Lecture 3- Part 1- Error Analysis
31:37
9. CS 182 Lecture 3- Part 2- Error Analysis
24:45
10. CS 182 Lecture 3- Part 3- Error Analysis
16:30
11. CS 182- Lecture 4- Part 1- Optimization
30:22
12. CS 182- Lecture 4- Part 2- Optimization
23:09
13. CS 182- Lecture 4- Part 3- Optimization
17:51
14. CS 182- Lecture 5- Part 1- Backpropagation
41:03
15. CS 182- Lecture 5- Part 2- Backpropagation
23:03
16. CS 182- Lecture 5- Part 3- Backpropagation
15:41
17. CS 182- Lecture 6- Part 1- Convolutional Networks
14:18
18. CS 182- Lecture 6- Part 2- Convolutional Networks
21:00
19. CS 182- Lecture 6- Part 3- Convolutional Networks
38:53
20. CS 182- Lecture 7- Part 1- Initialization, Batch Normalization
22:15
21. CS 182- Lecture 7- Part 2- Initialization, Batch Normalization
26:05
22. CS 182- Lecture 7- Part 3- Initialization, Batch Normalization
38:23
23. CS 182- Lecture 8- Part 1- Computer Vision
11:53
24. CS 182- Lecture 8- Part 2- Computer Vision
22:08
25. CS 182- Lecture 8- Part 3- Computer Vision
16:44
26. CS 182- Lecture 8- Part 4- Computer Vision
16:25
27. CS 182- Lecture 9- Part 1- Visualization and Style Transfer
25:29
28. CS 182- Lecture 9- Part 2- Visualization and Style Transfer
10:52
29. CS 182- Lecture 9- Part 3- Visualization and Style Transfer
19:11
30. CS 182- Lecture 10- Part 1- Recurrent Neural Networks
35:53
31. CS 182- Lecture 10- Part 2- Recurrent Neural Networks
20:38
32. CS 182- Lecture 10- Part 3- Recurrent Neural Networks
27:44
33. CS 182- Lecture 11- Part 1- Sequence to Sequence
21:55
34. CS 182- Lecture 11- Part 2- Sequence to Sequence
26:36
35. CS 182- Lecture 11- Part 3- Sequence to Sequence
21:30
36. CS 182- Lecture 12- Part 1- Transformers
13:51
37. CS 182- Lecture 12- Part 2- Transformers
25:38
38. CS 182- Lecture 12- Part 3- Transformers
23:53
39. CS 182- Lecture 13- Part 1- NLP
30:16
40. CS 182- Lecture 13- Part 2- NLP
11:15
41. CS 182- Lecture 13- Part 3- NLP
20:01
42. CS 182- Lecture 14- Part 1- Imitation Learning
18:04
43. CS 182- Lecture 14- Part 2- Imitation Learning
24:40
44. CS 182- Lecture 14- Part 3- Imitation Learning
08:35
45. CS 182- Lecture 15- Part 1- Policy Gradients
15:43
46. CS 182- Lecture 15- Part 2- Policy Gradients
16:16
47. CS 182- Lecture 15- Part 3- Policy Gradients
32:03
48. CS 182- Lecture 16- Part 1- Actor-Critic & Q-Learning
26:32
49. CS 182- Lecture 16- Part 2- Actor-Critic & Q-Learning
15:55
50. CS 182- Lecture 16- Part 3- Actor-Critic & Q-Learning
16:27
51. CS 182- Lecture 17- Part 1- Generative Models
33:23
52. CS 182- Lecture 17- Part 2- Generative Models
23:28
53. CS 182- Lecture 17- Part 3- Generative Models
24:21
54. CS 182- Lecture 18- Part 1- Latent Variable Models
27:51
55. CS 182- Lecture 18- Part 2- Latent Variable Models
13:33
56. CS 182- Lecture 18- Part 3- Latent Variable Models
17:02
57. CS 182- Lecture 18- Part 4- Latent Variable Models
17:21
58. CS 182- Lecture 19- Part 1- GANs
22:46
59. CS 182- Lecture 19- Part 2- GANs
28:14
60. CS 182- Lecture 19- Part 3- GANs
27:31
61. CS 182- Lecture 20- Part 1- Adversarial Examples
22:12
62. CS 182- Lecture 20- Part 2- Adversarial Examples
35:51
63. CS 182- Lecture 20- Part 3- Adversarial Examples
18:38
64. CS 182- Lecture 21- Part 1- Meta-Learning
21:51
65. CS 182- Lecture 21- Part 2- Meta-Learning
33:39
66. CS 182- Lecture 21- Part 3- Meta-Learning
14:58
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