Wiki of the Machine Learning / Deep Learning Pole

This is a selection of some references that may be useful to start or consolidate your knowledge. This information is of course not exhaustive and any suggestions for additions are welcome. If you have any questions, please send an email to

Reference publications

Books & Code Examples

Frameworks to start & to document in ML/DL

Online courses

Next conferences or training workshops

Previous conferences on ML/DL with astrophysical topics

Previous training workshops


For the presentations, the slides can be retrieved with the link; the videos can be viewed on
  • 25/03/2022 : François Lanusse (CosmoStat) " Probabilistic Deep Learning for Weak Lensing : from Mass-Mapping to Cosmological Parameter Inference " (
  • 04/05/2021 : Raoul Canameras (MPA-Garching) " Finding and modeling strong gravitational lenses with deep neural networks " (210504 Canameras)
  • 30/03/2021 : Alexandre Boucaud & Hubert Bretonnière (LAC) " FlowVAE: taking control of galaxy image simulations with deep generative networks "
  • 09/03/2021 : Laurent Risser (IMT) " Explainability techniques for black-box decision rules in Machine Learning " (210309 Risser)
  • 11/02/2021 : Sidonie Lefebvre (ONERA/DOTA) " Generative Adversarial Networks (GANs) : concept and application to cloudy sky images synthesis " (210211 Lefevbre)
  • 01/02/2021 : Nicolas Audebert (CNAM) " Hyperspectral remote sensing data analysis using Deep Learning " (210201 Audebert)
  • 18/01/2021 : François-Xavier Dupé (LIS/QARMA) " How Machine Learning can help to automate processing tasks ? An example with image denoising " (210118 Dupe)
  • 11/01/2021 : Julien Wojak (Institut Fresnel) " Deep Learning : focus on auto-encoder as a pre-processing step for classification " (210111 Wojak)

ML/DL pole staff & project implications

Available GPUs computing resources for LAM staff

  • GPU partition on the LAM cluster (responsible JC Lambert) : 7 GPUs RTX2080 + 11 GPUs A40
  • Aix-Marseille University Mesocentre cluster : several GPU partitions (volta partition : 20 GPUs V100)
  • How to submit a job on the Mesocentre cluster :
    • in /home/myaccount/ :
      copy the shell script source setup Meso and put it in this directory
    • in /home/myaccount/ :
      then follow the instructions in the file create MyVirtualEnvironment in order to create a virtual environment for Tensorflow
    • in /home/myaccount/(path_to_the_directory_of_my_code) :
      copy, modify the shell script myjob Meso and put it in this directory
      sbatch -p volta --gres=gpu:1 ./ (volta or pascal or kepler)
    • Be careful ! You don't need to specify the number of CPUs you want. It will be automatically determined by the amount of RAM you asked for. Therefore, the actual number of counted hours will be : number of CPUs * number of hours of your simulation.