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Deep Learning for Image Analysis

Deep learning has achieved formidable results in the image analysis field in recent years, in many cases exceeding human performance. This success opens paths for new applications, entrepreneurship and research, while making the field very competitive.

This course aims at providing the students with the theoretical and practical basis for understanding and using deep learning for image analysis applications.


Fall 2019 Edition: ATHENS MP10 (November 18-22)

Practical sessions set up

Download starting practical sessions structure:

Download shared material:

Invited speakers

  • Marc Huertas (Canaries Astrophysics Institute)
  • Maximilian Jaritz (
  • Olivier Moindrot (Owkin)

Teaching assistants

  • Eric Bazan
  • David Duque
  • Leonardo Gigli
  • Arthur Imbert
  • Tristan Lazard

Fall 2018 Edition: ATHENS MP10 (November 19-23)

Invited speakers

  • Pierre Fillard (Therapixel)
  • Marc Huertas-Company (Canaries Astrophysics Institute; Observatoire de Paris)
  • Bogdan Stanciulescu (CAOR, MINES ParisTech)
  • Pauline Luc (Facebook AI Research)

Teaching assistants

  • Robin Alais
  • Joseph Boyd
  • Leonardo Gigli
  • Peter Naylor
  • Robin Alais
start.1573986990.txt.gz · Last modified: 2019/11/17 11:36 by edecenciere
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