Welcome to the Weizmann practical Deep Learning Course 2024
All communication with the lecturers will be made via SLACK. Join here:
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 take-home assignment (>60%). The exact weight of the project and take-home assignment will be fixed so the class average will not exceed 90.
All lecture slides and tutorial code will be posted below.
| Date | Lecture | Slides & Video |
Tutorial | Slides & Video |
Tutorial 2 | Slides & Video |
|---|---|---|---|---|---|---|
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27/3/2025 |
Eilam: Introduction |
Etienne: Python essentials |
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Etienne: NumPy and grad. desc. |
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| 6/4/2025 |
Dmitrii: Backpropagation |
Etienne: pytorch (tut) |
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Etienne: homework 1 Classification |
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| 24/4/2025 |
Eilam: Convolutional NN |
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Nilotpal: CNNs (tut) |
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Dmitrii: Optimisation, |
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| 8/5/2025 |
Etienne: Autoencoders |
Dmitrii: VAE (tut) |
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Etienne: transfer learning homework 2 |
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| 15/5/2025 |
Eilam: CNN architectures and RNN |
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Eilam: GAN |
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Nilotpal: GAN tutorial |
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| 22/5/2025 |
Eilam: Graph Neural Networks |
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Etienne: GNN tutorial |
Etienne: homework 3: Graph Neural Networks |
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| 29/5/2025 |
Eilam (Recorded): Detection & Segmentation |
Etienne: UNET
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| 5/6/2025 |
Etienne: Attention is All You Need |
Eilam: GPT |
Eilam: |
Dmitrii: Homework 4: |
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| 12/6/2025 |
Dmitrii: Diffusion |
Dmitrii: Diffusion tutorial |
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| 26/6/2025 |
Eilam: Deep Reinforcement Learning |
Dmitrii: Deep Q-learning tutorial
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Etienne: Homework 5: policy gradient |
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| 3/7/2025 | Project Proposals I | |||||
| TBA | Project Proposals II | |||||
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TBA 10:00-12:00 |
Final exam | |||||
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TBA 11:00-16:00 |
Project Presentations (POSTERs festival) |