An ML Engineer with a dedication
to learning and problem-solving.
Here you can find some
of the personal projects I developed during the years.
You can find more about my actual professional experience in my CV.
Key Interests:
Interpretability and Safety in LLMs
An implementation of
“Proximal Policy Optimization”
using PyTorch.
The code allows to train and run
pre-trained reinforcement learning agents on Gym environments
(now called Gymnasium)
Developed With:
PyTorch 2.1, Gymnasium
C implementation of the A* algorithm.
The interface is built using the SDL2 library and allows to draw mazes, save and re-load them.
This project explores the optimality of A* providing a clear visualization of how the algorithm uses its heuristic to reach the goal as quickly as possible.
Developed With: C, SDL2
An LSTM-based architecture to train Sequence Labeling models.
The project shows that even a tiny LSTM with a tiny dataset can be a great low-requirements solution for small use-cases like DIY / home automation projects.
Developed with: PyTorch 2.1