Marc Huertas-Company (IAC) and Alexandre Boucaud (APC)
August 2023
- Introduction and neural networks recap from cycle 1 - HTML slides
- Probabilistic neural networks - PDF
- Convolutional networks - PDF
- Image2image networks and Transformers - PDF
- Attention mechanism and Graph networks - PDF
- Introduction to MLOps - HTML slides
- Slides to come soon
- Friday Zoom recording | Recording code: CqBP?5b!Y^
To run the notebooks locally, install the dependencies from the requirements.txt
python -m pip install -r requirements.txt
Warning
macOS users with M1/M2 processors please follow the instructions below to install TensorFlow (otherwise the notebook kernel will die at the beginning) Apple M1/M2 specific TensorFlow installation
Warning
For cycle 2 and 3, those not using Google Colab links must first run the dataset creation steps below before starting with the notebooks.
Instructions for the notebook:
- choose one simulation between
IllustrisTNG
(dataset version1.0.0
) andSIMBA
(dataset version1.0.1
) - execute Part 1 and 2 whose goal is to predict
$\Omega_M$ and try to improve the results of the MLP - try to apply the networks trained with one simulation to the other one
- move on to Part 3 where we try to predict
$\sigma_8$ and$\Omega_M$
An alternative is to try the notebook on Google Colab (need a Google account).
Warning
For cycle 2 and 3, those not using Google Colab links must first run the dataset creation steps below before starting with the notebooks.