This is a repository for Deep Learning course.
| № | Topic | Lecture | Seminar | Recording |
|---|---|---|---|---|
| 1 | Multi-layer perceptron. Gradient calculation | slides | - | record |
| 2 | NN optimization. Regularization | slides | ipynb | record |
| 3 | Weight initialization. Batch normalization. CNN | slides | ipynb | record |
| 4 | Recurrent neural networks. LSTM. GRU. DropOut in RNN. | slides | ipynb | record |
| 5 | Attention. Transformer. BERT. | slides | ipynb | record |
| 6 | Computer vision. Classification. Object detection | slides | ipynb | record |
| 7 | Semantic segmentation. Instance segmentation. | slides | ipynb | record |
| 8 | Reinforcement learning. V-, Q-functions. Belman equations. Value iteration. | slides | - | record |
| 9 | Monte-Carlo methods. Temporal learning. Q-learning. DQN. | slides | ipynb | record |
| 10 | Policy gradients. Actor-Critic algorithm. | slides | ipynb | record |
| 11 | Generative models. VAE | slides | ipynb | record |
| 12 | Autoregressive model. GAN. | slides | - | record |
| 13 | Graph Learning | slides | ipynb | record |