# AI4BioMed 2026 - Hands-on sessions installation instructions

NoteNote

This file is available at the following URL:

https://lhst.eu/ai4biomed/index.html

## 1. Install uv

Open a terminal and run:

    curl -LsSf https://astral.sh/uv/install.sh | sh

Then, open a new terminal and verify the installation:

    uv --version

Open a terminal (right click on the windows menu icon and select Terminal) then run:

    powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

After that, open a new terminal and verify the installation with:

    uv --version

## 2. Download notebooks and data

Download and extract the following archive:

https://lhst.eu/ai4biomed/hands_on.zip

## 3. Test installation

Open a new terminal at the root of the extracted archive and run:

    uv run test_install

Wait for the command to finish and check that you no error messages appear.

## 4. Launch Jupyter lab

Open a new terminal at the root of the extracted archive and run:

    uv run jupyter lab

A new tab should open in your browser with a local instance of Jupyter-lab. Otherwise, open the link that should have been displayed in your terminal with an url beginning with `http://localhost:8888/lab?token=...`.

## If something fails…

If for some reason one of the previous steps fails, you still have the possibility to work on the notebooks in Google Colab (you will need an active internet connexion and a Google account).

You can import the notebooks from the `ipynb` file on your computer by using Colab’s Import notebook feature, or you can open them directly by clicking these links:

- 01_tensors.ipynb
- 02_learning_rate.ipynb
- 03_data_normalization.ipynb
- 04_optimizers.ipynb
- 05_nn_regression.ipynb
- 06_activation_functions.ipynb
- 07_overfitting.ipynb
- 08_batch_dataset_dataloader.ipynb
- 09_metrics.ipynb
- 10_sign_mnist_example.ipynb
- 11_malaria_cells_example.ipynb

## Full notebooks

A repository with the full notebooks (with cell outputs and exercise solutions) is available here:

https://gitlab.in2p3.fr/jbarnier/ateliers_deep_learning