Deep Learning

תוכן הקורס

1. 19.1.1 introduction - part A25:14צפייה חופשית
2. 29.1.2 introduction - part B18:59
3. 39.1.3 introduction - available gpu18:10
4. 49.2.1 ANN - introduction16:09
5. 59.2.2 ANN - how it's work24:44
6. 69.2.3 ANN - apllications, advantages and disadvantages19:47
7. 79.3.1 tensorflow - mnist24:13
8. 89.3.2 tensorflow - ANN exercise18:45
9. 99.4.1 CNN - introduction17:27
10. 109.4.2 CNN - cnn architecture28:05
11. 119.4.3 CNN - example of implementation17:53
12. 129.5.1 RNN - introduction and architectutre21:24
13. 139.5.2 RNN - vanising and exploding gradient13:16
14. 149.5.3 RNN - implementation example12:02
15. 159.6.1 LSTM - introduction and architecture24:55
16. 169.6.2 LSTM - bidirectional and implementation10:03
17. 179.7.1 GAN - introduction19:42
18. 189.7.2 GAN - gan architecture21:37
19. 199.7.3 GAN - quick implementation14:15
20. 209.8.1 autoencoders - therory25:13
21. 219.8.2 autoencoders - implementation example23:19