This site collects resources to learn Deep Learning in the form of Modules available through the sidebar on the left. As a student, you can walk through the modules at your own pace and interact with others thanks to the associated Discord server. Then we hope you'll become a contributor by improving this site!
Marc Lelarge, Andrei Bursuc with Jill-Jênn Vie
Pre-requisites:
Mathematics: basics of linear algebra, probability, differential calculus and optimization
Programming: Python. Test your proficiency: quiz
Super fast track to learn the basics of deep learning from scratch:
Have a look at the slides of Module 1: Introduction & General Overview
Run the notebook (or in colab) of Module 2a: Pytorch Tensors
Run the notebook (or in colab) of Module 2b: Automatic Differentiation
Check the Minimal working examples of Module 3: Loss functions for classification. If you do not understand, have a look at the slides.
Have a look at the slides of Module 4: Optimization for Deep Learning
Try playback speed 1.5 for the video from Module 5: Stacking layers.
Run the notebook (or in colab) of Module 6: Convolutional Neural Network
Try playback speed 2 for the video from Module 7: Dataloading
Have a look at the slides of Module 8a: Embedding layers
Well done! Now you have time to enjoy deep learning!
Join the GitHub repo dataflowr and make a pull request. What are pull requests?
Thanks to Daniel Huynh, Eric Daoud, Simon Coste
Materials from this site is used for courses at ENS and X.
Last modified: March 23, 2023. Website built with Franklin.jl and the Julia programming language. 
