• Table of contents
• Wiki of the Machine Learning / Deep Learning Pole
  • Books & Code Examples
  • Frameworks to start & to document in ML/DL
  • Online courses
  • Some reference publications
  • Conferences / Training workshops
  • Seminars
  • Staff & Projects
  • Available GPUs computing resources for LAM staff

Wiki of the Machine Learning / Deep Learning Pole¶

INFORMATION WEB PAGE for the ML/DL Pole at CeSAM
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 morgan.gray@lam.fr

Books & Code Examples¶

• Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow by Aurélien Géron (O'Reilley editor, third edition)
  • Jupyter notebooks for the code samples : https://github.com/ageron/handson-ml3
• Deep Learning with Python by François Chollet (Manning editor) : a good presentation of Keras to make Deep Learning
  • Jupyter notebooks for the code samples : https://github.com/fchollet/deep-learning-with-python-notebooks
  • Examples of codes with several issues & neural networks : https://keras.io/examples/ ; https://keras.io/api/applications/
• Deep Learning by Goodfellow, Bengio, Courville (MIT press book editor) : a more theoretical book
  • free access per chapter : https://www.deeplearningbook.org/
• Probabilistic Deep Learning with Python, Keras and Tensorflow Probability by Durr, Sic, Murina (Manning editor)
• Bayesians methods for hackers, Probabilistic Programming and Bayesian Inference by Cameron Davidson-Pilon (Addison-Wesley Data & Analytics editor)
  • free access per chapter : https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/
• Pattern Recognition & Machine Learning by Christopher M. Bishop (Springer Editor)
  • free to download : http://mng.bz/oPWZ
• Statistics, Data Mining and Machine Learning in Astronomy by Ivezic, Connolly, Vanderplas & Gray (Princeton Press)
  • HTML documentation : https://www.astroml.org/ ; Source-code repository: https://github.com/astroML/astroML
• The Elements of Statistical Learning by Hastie, Tibshirani & Friedmman (Springer Editor)
  • free to download : https://web.stanford.edu/~hastie/ElemStatLearn/
• Last, but not least : https://github.com/ChristosChristofidis/awesome-deep-learning?tab=readme-ov-file#books

Frameworks to start & to document in ML/DL¶

• ScikitLearn : https://scikit-learn.org/stable/user_guide.html
• Keras : https://keras.io/getting_started/ ; https://keras.io/api/ ; https://keras.io/guides/
• Tensorflow : https://www.tensorflow.org/api_docs/python/tf
• Pytorch : https://pytorch.org/get-started/locally/ ; https://pytorch.org/tutorials/
• Last, but not least : https://github.com/ChristosChristofidis/awesome-deep-learning?tab=readme-ov-file#frameworks

Online courses¶

• FIDLE 24/25 (in French) : Introduction au Deep Learning (un très bon cours !)
  • Programme : https://fidle.cnrs.fr/w3/parcours/01-decouverte.html ; https://fidle.cnrs.fr/w3/parcours/02-avance.html
  • Videos : https://www.youtube.com/watch?v=iUGTPcyYCPM&list=PLlI0-qAzf2Sbc7QL2u-GcLykggE2aIWJW
  • Supports : https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle
• CNAM (in French) :
  • Réseaux de neurones : slides Formation CNAM ; videos : send an email request
  • Modélisation descriptive et introduction aux réseaux de neurones http://cedric.cnam.fr/vertigo/Cours/ml/
  • Modélisation décisionnelle et apprentissage profond http://cedric.cnam.fr/vertigo/Cours/ml2/
• STANFORD University (in English) :
  • Machine Learning : https://www.coursera.org/learn/machine-learning#syllabus ; slides Formation COURSERA
  • CNN for Visual Recognition : https://cs231n.github.io/
  • Videos : https://www.youtube.com/playlist?list=PLoROMvodv4rOmsNzYBMe0gJY2XS8AQg16
• GeeksforGeeks (in English) :
  • https://www.geeksforgeeks.org/machine-learning/machine-learning/
  • https://www.geeksforgeeks.org/machine-learning/ai-ml-and-data-science-tutorial-learn-ai-ml-and-data-science/
• ML/DL Mastery (in English) : https://machinelearningmastery.com/start-here/
• Last, but not least : https://github.com/ChristosChristofidis/awesome-deep-learning?tab=readme-ov-file#courses

Some reference publications¶

• General comments on :
  • Machine Learning : Baron(19) MLinAstronomyOverview & Mehta MLforPhysicists
  • A glossary that defines general ML terms : https://developers.google.com/machine-learning/glossary#a
  • Deep Learning : Hadji(18) CNN-Overview
• Some classical Neural Network architectures :
  • Simonyan VGGNet - He(15) ResNet - He(16) ResNet - He(18) Mask-R-CNN
  • Szegedy(14) Inception - Szegedy(15) RethinkingInception - Szegedy(16) InceptionV4 InceptionResNet - Chollet(16) Xception - Hu(19) Squeeze-ExcitationNetworks
  • Vaswani(17) AttentionIsAllYouNeed-Transformers - Dosovitskiy(21) Transformer-ImageRecognitionAt Scale
  • Makhzani AdversarialAutoencoders - Goodfellow GenerativeAdversarialNets
  • Parker(24) AstroCLIP - Parker(25) AION-1
• Practical advice (methodology, specific aspects) :
  • Bengio(12) PracticalRecommendations - Smith(18) Hyperparameters - OptimizerAlgorithms - Dropout - BatchNormalization

Conferences / Training workshops¶

• CNRS School 2027, AstroInformatics Thematic School :
• CNRS School 2025, AstroInformatics Thematic School : https://gitlab.in2p3.fr/astroinfo/ecole-astroinfo-2025
• 2025, Bayesian Deep Learning for Cosmology and Time Domain Astrophysics : https://indico.in2p3.fr/event/35882/overview
• Forum Orap 2024 (in French) HPC et simulation à l'heure de l'IA : défis et opportunités :
  • slides & videos : https://orap.irisa.fr/forum-30-ans/
• Colloque MITI 2024 (in French) Jumeaux numériques, nouvelles frontières :
  • videos : https://miti.cnrs.fr/wp-content/uploads/2024/04/Colloque_jumeaux_numerique_programme_interactif.pdf
• IAP 2023, Debating the Potential of Machine Learning in astronomical surveys : https://indico.iap.fr/event/1/timetable/#20231127.detailed
• CNRS School 2023, AstroInformatics Thematic School : https://astroinfo2023.sciencesconf.org/ ; https://gitlab.in2p3.fr/astroinfo/ecole-astroinfo-2023
• ASNUM 2002, Journées de l'Action Spécifique Numérique Astrophysique : https://asnum2022.sciencesconf.org/ (videos)
• 2022, LISA data analysis, From classical methods to Machine Learning : https://indico.in2p3.fr/event/27706/program (for the slides & videos, send an email request)
• 2022, Bayesian Deep Learning for Cosmology & Time Domain Astrophysics : https://astrodeep.net/workshop2022/ (videos)
• ESO/ESA 2022, SciOps workshop on Artificial Intelligence for Science and Operations in Astronomy : https://www.eso.org/sci/meetings/2022/SCIOPS2022/program.html (only slides)
• CNRS School 2021, AstroInformatics Thematic School : https://astroinfo2021.sciencesconf.org/ ; https://gitlab.in2p3.fr/astroinfo/ecole-astroinfo-2021
• IAP 2021, Debating the potential of ML in astronomical surveys : https://ml-iap2021.sciencesconf.org/browse/session (videos)
• CIRM 2021 (in English) : Mathematics, Signal Processing and Learning ​ (more theoretical courses with a mathematical view & practical sessions)
  • https://conferences.cirm-math.fr/2472.html
  • slides : https://www.i2m.univ-amu.fr/seminaires_signal_apprentissage/Conf/Jan2021/index.php
  • videos : https://library.cirm-math.fr/ListRecord.htm?list=request&table=3&NumReq=115&cluster_1=2472
• Ecole CNRS 2020 (in French) : ANF Machine Learning pour Informaticiens, bases et enjeux du Machine Learning et du Deep Learning : https://gitlab.in2p3.fr/ri3/ecole-info/2020/anf-machine-learning/-/tree/master/notebooks
• Bayesian Deep Learning for Cosmology & Gravitational waves : https://astrodeep.net/workshop2020/#schedule (videos)
• JDEV 2020 (in French) :
  • Python, préparation des données avec Panda & Apprentissage Automatique avec Scikitlearn : https://gitlab.in2p3.fr/aboucaud/atelier-jdev-2020
  • Fondamentaux Apprentissage Automatique avec Python & ScikitLearn : http://laurent.risser.free.fr/TMP_SHARE/JDEV2020/
  • Introduction Keras & Tensorflow : https://gricad-gitlab.univ-grenoble-alpes.fr/talks/fidle/
  • Introduction Deep Learning & PyTorch : http://laurent.risser.free.fr/TMP_SHARE/JDEV2020_T8_AP01/

Seminars¶

For the presentations, the slides can be retrieved with the link

• 21/09/2026 : David Cornu (OPM) " Galaxy detection with Deep Learning in radio data " (260921 Cornu)
• 01/06/2026 : Sara El Bouch (OCA) " Contrastive Learning for Open-Set Anomaly Detection of Radio Transients " ()
• 11/05/2026 : Anne-Laure Mealier (CentraleDigitalLab) " Foundation Models and Deep Learning for Astrophysics " (260511 Mealier)
• 27/04/2026 : Adeline Paiement (LIS) " Domain-informed analysis of astrophysics data " (260427 Paiement)
• 08/01/2026 : Jonathan Dray (LAM) " J. Nousiainen article presentation " (260108 Notes+260108 Article)
• 16/06/2025 : Romain Brunet (LAM) " Direct weak lensing shear measurements with deep learning " (250616 Brunet)
• 28/04/2025 : Ronan Sicre (LIS) " Visual interpretability: saliency maps and interpretable classification " (250428 Sicre)
• 24/02/2025 : Servis Konstantinos (LAM) " Who threw that rock? " (250224 Servis)
• 26/11/2024 : Marc Huertas-Company " Deep Learning the physics of galaxy formation " (https://www.lam.fr/en/event/lam-seminar-marc-huertas-company/)
• 23/09/2024 : Loris Berthelot (LIS/LAM) " Towards an unbiased detection of Galactic filaments using innovative Deep Learning methods " (240923 Berthelot)
• 01/07/2024 : Maxime Dumont (LAM/ONERA) " Physics Informed Neural Network & VAE for phasing space segmented telescope " (240701 Dumont)
• 15/04/2024 : Camille Godbillot (CEREGE) " Towards the use of CNNs to constrain the phytoplankton community response to environmental changes: an example from the Mediterranean Sea " (240415 Godbillot)
• 11/03/2024 : Hugo Vivien (LAM) " Panopticon, Detecting transits in PLATO lightcurves " (240311 Vivien)
• 08/01/2024 : Adeline Paiement (LIS) " Physics-informed Deep Neural Network for characterising galaxy morphology " (240108 Paiement)
• 04/12/2023 : Reda Ait Ouhamed (LAM) " Galaxy redsfhit estimation from multi-band images with Deep Learning " (231204 Reda)
• 20/11/2023 : Raoul Canameras (MPA-Garching) " Supervised Deep Learning methods for Rubin LSST, lmage classification, mass modeling and photometric redshift prediction " (231120 Canameras)
• 05/04/2023 : Eric Wulff (CERN) " Hyperparameter Optimization for DL using HPC" (230405 Wulff)
• 24/11/2022 : Maxime Quesnel (University of Liège) " A Simulator-based Autoencoder approach for Focal-Plane Wavefront Sensing" (221124 Quesnel)
• 25/03/2022 : François Lanusse (CosmoStat) " Probabilistic Deep Learning for Weak Lensing : from Mass-Mapping to Cosmological Parameter Inference " (https://eiffl.github.io/LAM2022/)
• 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) " 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)
  (The videos of the first seminars can be viewed on https://seminars.lam.fr/#MLDL)

Staff & Projects¶

• Staff (FTE) : Morgan Gray (100%) ; Jean-Charles Meunier (75%)
• Projects :
  • ANR APPLY (GRD): https://sites.google.com/view/anr-apply/home ; Deep Learning for PSF estimation GRAY(22) DeepLoop
  • ANR AI4AO (GRD): https://sites.google.com/view/ai4ao/home
  • EUCLID (ESA) : https://www.euclid-ec.org/science/overview/ ; https://sci.esa.int/web/euclid/-/science
  • AZIMOV (GRD/ONERA): Phasing Segmented Telescopes with Deep Learning Dumont(24) PhasingSegmentedTelescopes DL
  • BigSF (GECO/LIS): Supervised Machine Learning on Galactic filaments Zavagno(22) Supervised ML GalacticFilaments ; Berthelot(24) Supervised ML GalacticFilaments
  • FSOC (GRD/ALPAO) : Reinforcement Learning for Free Space Optical Communication
  • PAPYRUS (GRD/France2020): Reinforcement Learning for PAPYRUS Camelo(23) AO4ELT7 ; Camelo(24) SPIE
  • PETALING (GRD/France2030): Estimating the ELT’s Differential Pistons with Deep Learning Chauvet(23) AO4ELT7 ; Janin(24) SPIE-Petaling ; Janin(25) A&A-Petaling
  • DOSSA (GRD/Unistellar/France2030): Janin(24) SPIE-SpaceSurveillance
• Poster of the ML/DL pole at the LAM/ONERA/FRESNEL day (16/06/2022): Gray PosterPoleMLDL-A4
• Presentation of the ML/DL pole at the OSU-AI scientific day (20/04/2022): Gray JourneeIA-OSU
• Presentation of the ML/DL pole to the LAM scientific council : CeSAM__2020-v3.pdf
• Proposal for the creation of the ML/DL pole : Visioconférence du 09/10/2020

Available GPUs computing resources for LAM staff¶

• GPU partition on the LAM cluster (responsible JC Lambert) : 7 GPUs RTX2080 + 11 GPUs A40
  https://infrastructure.docs.lam.fr/cluster/partitions/gpu/
• Aix-Marseille University Mesocentre cluster : several GPU partitions (volta partition : 20 GPUs V100)
  https://mesocentre.univ-amu.fr/appel-a-projets/
  https://mesocentre.univ-amu.fr/les-tutoriaux/
  https://mesocentre.univ-amu.fr/copernicus/login.html
• MESONET, in which AMU is a partner, aims to deploy a set of HPC resources accessible to all academic users working in France : https://www.mesonet.fr/