Second-year Python notebooks, moving deeper into data science, numerical methods, and early machine-learning work. This folder includes normal TP notebooks, final/submitted notebooks, an IA/ folder (Artificial Intelligence training), and bundled datasets like MNIST and CIFAR.
The quirk here is weight : some notebooks are light classroom exercises, while others expect fairly chunky local datasets. Keep DATA_MNIST/, DATA_CIFAR/, and the CSV files where they are unless you enjoy debugging broken relative paths.

jupyter notebook R404_TP1a_student.ipynb

For the AI side, start in IA/. For the more data-analysis side, pick the top-level TP notebook matching the course/session.