Feel free to contact me if you spot some mistakes in my courses!
Courses
|
SLIDES
Deep Learning 101 for French engineering undergraduates.
A short lecture (1h30) about applying gradient backpropagation, gradient descent, loss functions, and introducing all the basic deep learning gear. Part of a wider series of lectures by Baptiste Pesquet at ENSC. > Language: French. > Student level: Master I > Created: 2024 |
|
|
NOTEBOOK
Deep Learning 101 for French engineering undergraduates (pratical course).
A Jupyter notebook to code deep learning's classic algorithms (almost) from scratch in Python. Part of a wider series of pratical courses by Baptiste Pesquet at ENSC. > Language: English. > Student level: Master I > Created: 2023 |
|
|
SLIDES
Introduction to timeseries analysis and modelization for French engineering undergraduates.
A very quick and dirty introduction to timeseries analyis basics (autocorrelation function, decomposition, basic modeling using ARIMA or HMM). Meant to list historical solutions to simple problems. > Language: French. > Student level: Master II > Created: 2022 |
|
|
NOTEBOOK
Recurrent Neural Networks basics in Pytorch for French engineering undergraduates.
A small pratical course detailing the typology of Recurrent Neural networks and how to apply them to toy use-cases using Pytorch. > Language: English. > Student level: Master II > Created: 2023 |
Tutorials
|
NOTEBOOK
An Introduction to Reservoir Computing with reservoirpy.
A demonstration of Reservoir Computing basic principles and applications and a walkthrough of reservoirpy basics. > Language: English. > Created: 2021 |
My “I want to teach Deep Learning” toolbox (acknowledgments)
I made the courses I publish here myself, but of course I built them based on the work of numerous other people. The list of acknowledgment would be big, and I admit it was a bit lost in the process, but here is a short list of some amazing people and contents that helped me build (directly or without knowing it) these teaching materials:
> Baptiste Pesquet and his magnum opus, that I enjoyed as a student and now as a collaborator.
> Xavier Hinaut, who provided me with building blocks for some of my courses.
> Nicolas Rougier and his ultimate guides through Matplotlib and reproducibility (the Matplotlib cheatsheets saved my life so many times now).
> Jason Brownlee and his amazing website.
> Mantas Lukoševičius, who helped me make sense of Reservoir Computing with his astonishingly simple guide.
> Christopher Olah and his amazing blog which I always show to students.
> The great initiative of MIT OpenCourseWare, making MIT courses available to anyone. I was particularly helped by the theoretical courses of Peter Kempthorne on timeseries analysis.
> Andrej Karpathy, who we do not present anymore. Just clicked on everything he does here.