This course provides a comprehensive exploration of modern deep learning techniques, from foundational concepts to advanced topics such as computer vision systems and large language models.
- Introduction to Neural Networks: Initialization, Optimization, Regularization
- Computer Vision
- Natural Language Processing
- Reinforcement Learning
- Generative Models: VAE, GAN, Diffusion, Flow Matching
- Advanced NLP: LLM, RAG, Agents
- Acceleration: Compilation, Quantization, Distillation
| Week # | Date | Topic | Lecture | Seminar | Recording |
|---|---|---|---|---|---|
| 1 | September, 9 | MLP, Backpropagation | slides, slides with notes | ipynb | record |
| 2 | September, 16 | Optimization, Regularization | - | - | - |
| 3 | September, 23 | Initialization, Normalization, CNN | - | - | - |
| 4 | September, 30 | - | - | - | - |
| 5 | October, 7 | - | - | - | - |
| 6 | October, 14 | - | - | - | - |
| 7 | October, 21 | - | - | - | - |
| 8 | October, 28 | - | - | - | - |
| 9 | November, 4 | - | - | - | - |
| 10 | November, 11 | - | - | - | - |
| 11 | November, 18 | - | - | - | - |
| 12 | November, 25 | - | - | - | - |
| 13 | December, 2 | - | - | - | - |
| 14 | December, 9 | - | - | - | - |
| Homework # | Date | Deadline | Description | Link |
|---|---|---|---|---|
| 1 | 08.09 | 29.09 | Autograd implementation | google form |
| 2 | 08.09 | 13.10 | Alexnet implementation on PyTorch | google form |
| 3 | 08.09 | 28.10 | Image captioning with attention | google form |
| 4 | - | - | - | - |
| 5 | - | - | - | - |
| 6 | - | - | - | - |
- 6 Homeworks = 70 points
- Oral Exam = 30 points
- Maximum Points: 70 + 30 = 100 points
- Probability Theory + Statistics
- Machine Learning
- Python