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Contrary to popular belief, the underpinnings of deep learning are rooted in surprisingly simple mathematics. Through a clever combination of functions and parameters, deep neural networks manage to discern patterns in vast amounts of data.
Virtual Place
Date
Partners
The basics of AI: The Elegant Simplicity of Deep Neural Networks
Contrary to popular belief, the underpinnings of deep learning are rooted in surprisingly simple mathematics. Through a clever combination of functions and parameters, deep neural networks manage to discern patterns in vast amounts of data. This presentation will unveil these foundational equations and showcase how something so straightforward powers cutting-edge innovations in artificial intelligence.
About Professor Gilles Louppe (Montefiore Institute of Electrical Engineering and Computer Science) :
His research topics are at the intersection of deep learning, approximate inference and the physical sciences. Together with his collaborators and students, they have been developping a new generation of simulation-based inference algorithms based on deep learning, with several applications in particle physics, astrophysics, astronomy and gravitational wave science. Their long-term research objective is make AI a cornerstone of the modern scientific method.
Contact person: Marjorie Ranieri (marjorie.ranieri@uliege.be)
If you are interested in the recording of this session, please check the gallery section!
UNIC CityLabs
Digital transition | Artificial Intelligence
seminar | Seminar
English