Try to implement some Recommendation Algorithm with PyTorch to learn both.

• Poetry 1.8.3
• Miniconda or alternatives that can create new Python environment with a specified Python version

• Create a new .env file based on .env.example and populate the variables there
• Create a new Python 3.11.9 environment: conda create --prefix .venv python=3.11.9
• Make sure Poetry use the new Python 3.11.9 environment: poetry env use .venv/bin/python
• Install Python dependencies with Poetry: poetry install

• Start MLflow locally: make mlflow-up
• Start the Jupyterlab notebook: make notebook-up
• Run the data prep notebooks in this order: 000, 001, 002.
• Then you can run the notebooks from 010 to see how the algorithms fit with the prep data.
• Experiment evaluation should be available via MLflow web UI at localhost:5003 if you have run make mlflow-up.

• Run notebooks 020 to 025