This course provides a comprehensive exploration of modern deep learning techniques, from foundational concepts to advanced topics.
- Introduction to Neural Networks: MLP, Backpropagation, Initialization, Optimization, Regularization, CNN
- Natural Language Processing: Word Embeddings, RNN, LSTM, Attention, Transformer, LLM, Agents
- Computer Vision: Classification, Object detection, Segmentation
- Generative Models: Autoregression, VAE, GAN, Diffusion, Diffusion Models, Flow Matching, Multimodality
- Advanced: Reinforcement Learning, Deep Learning Theory, Acceleration
- Eduard Vladimirov @Edyarich
- Daniil Dorin @DorinDaniil
- Nikita Kiselev @kisnikser
- Alexey Kravatskiy @alexlegeartis
- Vadim Kasiuk @KasiukVadim
| Week # | Date | Topic | Lecture | Seminar | Recording |
|---|---|---|---|---|---|
| 1 | September, 8 | MLP, Backpropagation | lecture | seminar | record |
| 2 | September, 15 | Optimization, Regularization | lecture | seminar | record 2025 |
| 3 | September, 22 | Initialization, Normalization, CNN | lecture | seminar, notes | TBA |
| 4 | September, 29 | TBA | TBA | TBA | TBA |
| 5 | October, 6 | TBA | TBA | TBA | TBA |
| 6 | October, 13 | TBA | TBA | TBA | TBA |
| 7 | October, 20 | TBA | TBA | TBA | TBA |
| 8 | October, 27 | TBA | TBA | TBA | TBA |
| 9 | November, 3 | TBA | TBA | TBA | TBA |
| 10 | November, 10 | TBA | TBA | TBA | TBA |
| 11 | November, 17 | TBA | TBA | TBA | TBA |
| 12 | November, 24 | TBA | TBA | TBA | TBA |
| 13 | December, 1 | TBA | TBA | TBA | TBA |
| 14 | December, 8 | TBA | TBA | TBA | TBA |
| Homework # | Date | Deadline | Description | Link |
|---|---|---|---|---|
| 1 | 09.09 | 30.09 | Autograd implementation | google form |
| 2 | TBA | TBA | TBA | TBA |
| 3 | TBA | TBA | TBA | TBA |
| 4 | TBA | TBA | TBA | TBA |
| 5 | TBA | TBA | TBA | TBA |
- 14 Quizzes = 14 points
- 5 Homeworks = 50 points
- Oral Exam = 40 points
- Maximum Points: 14 + 50 + 40 = 104 points
- Probability Theory + Statistics
- Machine Learning
- Python