Deep Learning in Practice

MVA Master, January-March 2023

News : (reload for fresher news...)
- Contents remain accessible upon registration

The previous course (January-March 2022) can be found there; this course follows more or less the same lines, though not exactly.

General information about the course: presentation slides

To get access the recordings of this course, register here (NB: this is not the registration to the 2024 course, which is there.)

Requirements : having already followed a course about neural networks (this is an advanced deep learning course).
Typical mathematical notions used: differential calculus, Bayesian statistics, analysis, information theory.

Teaching team :
In a nutshell:
Chapter Title Summary Notes Videos Exercises
1 Deep learning vs. classical ML and optimization html pdf 1 [2022] Hyperparameter and training basics
Environment and practical tips and tricks
2 Interpretability html pdf 1, 2 1, 2, 3, 4 Visualization with grad-CAM
3 Architectures html pdf 1, 2 1, 2, 3 Graph-NN
4 Small data: weak supervision, transfer, and incorporation of priors html pdf 1, 2 Transfer learning: jupyter notebook
5 Modeling: deep learning and physics (exploiting known invariances, priors or physical properties) html pdf 1, 2, + refs by A. Bucci 1, 2, 3 Learning dynamical systems (Credits: Alessandro Bucci)
6 Generative models
(GAN, VAE, Normalizing Flows and Diffusion Models)
- pdf 1, 2
Generative models
7 Modeling tasks and losses +
Auto-ML / Auto-DeepLearning by Lisheng Sun-Hosoya and Romain Égelé from Isabelle Guyon's team
html pdf,
AutoML/MetaLearning slides,
Neural Architecture Search slides
1, 2 -

Program :

Ressources: links to introduction to python, numpy, classical machine learning + online deep learning courses

Internship offers :

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