Welcome to the Weizmann Practical Deep Learning Course 2025
All communication with the lecturers will be made via SLACK: join slack channel
Your lecturers and TAs are
Etienne Dreyer, Nilotpal Kakati, Dmitrii Kobylianskii and Prof Eilam Gross.
The grading system is based on your mandatory homework assignments (10%), a project (<30%), and a final exam (>60%). The exact weight of the project and exam will be fixed so the class average will not exceed 90.
All lecture slides and tutorial code will be posted below. Recordings are available on the Panopto page.
| Date | Lecture | Material | Tutorial 1 | Material | Tutorial 2 | Material |
|---|---|---|---|---|---|---|
|
27/3/2025 |
Eilam: Introduction |
Introduction Lecture |
Etienne: Python essentials |
Etienne: NumPy and grad. desc. |
||
| 3/4/2025 |
Dmitrii: Backpropagation |
Lecture 2: Backpropagation |
Etienne: pytorch (tut) |
Etienne: homework 1 Classification |
homework 1 | |
| 10/4/2025 |
Eilam: Convolutional NN |
Nilotpal: CNNs (tut) |
Dmitrii: Optimisation, |
Notebook | ||
| 24/4/2025 |
Eilam: GAN |
GAN 2025 Lecture |
Nilotpal: GAN (tut) |
Etienne: transfer learning homework 2 |
||
| 8/5/2025 |
Eilam: CNN architectures and RNN |
|
Etienne: Autoencoders |
|
Dmitrii: VAE (tut) |
|
| 15/5/2025 |
Eilam: Graph Neural Networks |
|
Etienne: GNN tutorial |
Etienne: homework 3: GNN |
||
| 22/5/2025 |
Eilam: Detection & Segmentation |
Etienne: UNET
|
TBA |
|
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| 29/5/2025 |
Dmitrii: Diffusion |
Dmitrii: Diffusion tutorial |
|
|
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| 5/6/2025 |
Etienne: Transformers |
Eilam: GPT |
|
Dmitrii: Homework 4: |
|
|
| 12/6/2025 |
Eilam: Deep Reinforcement Learning |
Dmitrii: Deep Q-learning tutorial
|
Etienne: Homework 5: policy gradient |
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| 19/6/2025 | Project Proposals I | |||||
| 26/6/2025 | Project Proposals II | |||||
| 3/7/2025 | Project Proposals III | |||||
|
TBA 10:00-12:00 |
Final exam | |||||
|
TBA 11:00-16:00 |
Project Presentations (POSTERs festival) |