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 | lecture, seminar |
| 3 | Weight initialization. Batch normalization. CNN | slides | ipynb | lecture, seminar |
| 4 | Recurrent neural networks. LSTM. GRU. DropOut in RNN. State Space Models. | slides | ipynb | |
| 5 | Attention. Transformer. BERT. | slides | ipynb | |
| 6 | Computer vision. Classification. Object detection | slides | ipynb | lecture, seminar |
| 7 | Semantic segmentation. Instance segmentation. | slides | ipynb | |
| 8 | Reinforcement learning. V-, Q-functions. Bellman equations. Value iteration. LSVI. Deep Q-Network. | slides_1, slides_2 | - | lecture |
| 9 | Monte-Carlo methods. Temporal learning. Q-learning. Policy gradients. Actor-Critic algorithm. | slides_1, slides_2 | ipynb | lecture + seminar |
| 10 | Graph Learning | slides | ipynb | |
| 11 | Generative models. VAE | slides | ipynb | |
| 12 | Autoregressive model. GAN. | slides | - | |
| 13 | Multimodal Learning | slides | - |