DeepLearn 2026
13th International School on Deep Learning
Orléans, France · July 20-24, 2026
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UPDATE
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IN DEEP LEARNING

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Early registration: October 14, 2025

SCOPE

DeepLearn 2026 will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning.

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ADDRESSED TO

Graduates, postgraduates and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, hence people less or more advanced in their career will be welcome as well.

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VENUE

DeepLearn 2026 will take place in Orléans, located in the heart of the Loire Valley, which was declared by UNESCO a World Heritage Site in 2000.

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STRUCTURE

  • 3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
  • All lectures will be videorecorded. Participants will be able to watch them again for 45 days after the event.
  • An open session will give participants the opportunity to present their own work in progress in 5 minutes. Also companies will be able to present their industrial developments for 10 minutes.
  • The school will include a hackathon, where participants will be able to work in teams to tackle several machine learning challenges.

Full live online participation will be possible. The organizers highlight, however, the importance of face to face interaction and networking in this kind of research training event.

Special Sessions

OPEN SESSION

An open session will collect 5-minute voluntary oral presentations of work in progress by participants.

They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by July 12, 2026.

INDUSTRIAL SESSION

A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry.

Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event.

Abstracts have to be submitted to david@irdta.eu by July 12, 2026.

HACKATHON

A hackathon will take place, where participants can voluntarily work in teams to tackle several machine learning challenges. They will be coordinated by Professor Sergei V. Gleyzer (University of Alabama). The challenges will be released 2 weeks before the beginning of the school. A jury will judge the submissions and the winners of each challenge will be announced by the end of August 2026. The winning teams will receive a modest monetary prize and the runners-up will get a certificate.

Sponsors

Companies/institutions/organizations willing to be sponsors of the event can download the sponsorship leaflet from sponsoring page

ORGANIZING COMMITTEE

  • Karim Abed-Meraim (Orléans, local co-chair)
  • Sergei V. Gleyzer (Tuscaloosa, hackathon chair)
  • Meryem Jabloun (Orléans, local co-chair)
  • Carlos Martín-Vide (Tarragona, program chair)
  • Santiago Montes (Tarragona, webpage)
  • Sara Morales (Luxembourg, finances)
  • Philippe Ravier (Orléans, local chair)
  • David Silva (London, organization chair)

CERTIFICATE

  • A certificate of successful participation will be delivered indicating the number of hours of academic activities (40). This should be sufficient for those participants who plan to request ECTS recognition from their home university.

Questions and further information

Mail to: david@irdta.eu

Acknowledgments

CO-ORGANIZERS

Université d’Orléans

Collège Doctoral Centre-Val de Loire

Institute for Research Development, Training and Advice – IRDTA, Luxembourg/London

Active links
  • AIces 2026
Past links
  • DeepLearn 2025
  • DeepLearn 2024
  • DeepLearn 2023 Summer
  • DeepLearn 2023 Spring
  • DeepLearn 2023 Winter
  • DeepLearn 2022 Autumn
  • DeepLearn 2022 Summer
  • DeepLearn 2022 Spring
  • DeepLearn 2021 Summer
  • DeepLearn 2019
  • DeepLearn 2018
  • DeepLearn 2017
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