Description

Deep Learning becomes more and more popular with algorithms based on these techniques performing very well for a variety of problems and scientific communities. The course has been designed to provide an introduction to deep learning techniques, including neural networks, computer vision, natural image processing and reinforcement learning. In particular, it will cover the fundamental aspects and the recent developments in deep learning: Feedforward networks and their optimisation and regularisation, representation learning and siamese networks, Generative Adversarial Networks and transfer learning, recurrent networks and Long Short Term Memory units, transformers, deep Reinforcement Learning.

Learning objectives
The course aims to introduce students to the design of deep learning methodologies both in theory and practice. We expect that by the end of the course, the students will:
• have knowledge of state-of-the-art deep learning techniques
• have a deeper understanding of deep learning methods
• have practical experience with deep learning frameworks

General Information

Program and Classrooms
04/10 9:00-12:15 Introduction (Auditorium 1 Michelin, Eiffel)
11/10 9:00-12:15 Optimization (Amphi e.093, Bouygues)
18/10 9:00-12:15 Generative Adversarial Networks (Amphi e.093, Bouygues)
25/10 9:00-12:15 Unsupervised /Self-supervised/ Few shot learning (Amphi e.093, Bouygues)
08/11 9:00-12:15 Natural Language Processing (Amphi e.093, Bouygues)
09/12 13:45-17:00 Reinforcement Learning (Amphi e.068, Bouygues)
16/12 13:45-17:00 Introduction to Computer Vision (Amphi e.093, Bouygues)
6/1 13:45-17:00 Diffusion Models & Poster session (Amphi F2.09 (Ferrié), Bréguet.)

Announcements

Announcements are not public for this course.
Staff Office Hours
NameOffice Hours
Sagar Verma
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Maria Vakalopoulou
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Vincent Lepetit
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David Picard
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Marin Scalbert
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mathieu.aubry@enpc.fr
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Doriand Petit
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Théo Moutakanni
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Asma BRAZI
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Video Recordings

Video Recordings
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