| Topic |
| Session 1 | A review of the basics of neural networks |
| Session 2 | Stochastic gradient descent |
| Session 3 | Residual connectivity and activation normalization |
| Session 4 | RNN |
| Session 5 | Word embedding |
| Session 6 | Transformer |
| Sesson 7 | Reinforcement learning |
| Session 8 | Language generation I |
| Session 9 | Language generation II |
| Session 10 | VAE - Image generation - |
| Session 11 | Diffusion model 1 - Image generation - |
| Session 12 | Diffusion model 2 -Image generation- |
| session 13 | GAN - Image generation - |
| Session 14 | Comprehensive exercise |
**This content is based on April 1, 2025. For the latest syllabus information and details, please check the