| Week | Lecture notebooks | Supplementary materials | Homework | Seminars |
|---|---|---|---|---|
| 1 | Lecture 1. Deep learning basics [GitHub] Lecture 2. Convolutional neural networks [GitHub] Lecture 3. Training better [GitHub] |
HW1 (Deadline: April, 14, 2023, 23:59 MSK) |
Seminar 2 Seminar 3: logging practice and regularizations examples |
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| 2 | Lecture 4. CV tasks [GitHub] Lecture 5. Modelling sequences [GitHub] Lecture 6: Vision Transformers [GitHub] |
Seminar 4: object detection and semantic segmentation + solution demos | ||
| 3 | Lecture 7. Graph Neural Networks [GitHub] Lecture 8. General tricks for efficient training [GitHub] Lecture 9. Training large models [GitHub] |
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| 4 | Lecture 10. Contrastive learning / self-supervised learning [GitHub] Lecture 11: One-shot/Zero-shot/Few-shot learning [GitHub] Lecture 12: Adversarial attacks and training [GitHub] |
HW2 (Deadline: May, 1, 2023, 23:59 MSK) |
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| 5 | Lecture 13: Generative models I (Autoregressive models and VAE) [GitHub] Lecture 14: Generative models II (Generative adversarial models) [GitHub] Lecture 15: Generative models III (Score-based and diffusion models) [GitHub] |
HW3 (Deadline: May, 17, 2023, 23:59 MSK) |
Seminar 8. VAE |