diff --git a/README.md b/README.md
index 7295355..9a32065 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,10 @@
-![stability-stable](https://img.shields.io/badge/stability-stable-green.svg)
 [![test](https://github.com/HelmholtzAI-Consultants-Munich/XAI-Tutorials/actions/workflows/test_notebooks.yml/badge.svg)](https://github.com/HelmholtzAI-Consultants-Munich/XAI-Tutorials/actions/workflows/test_notebooks.yml)
 [![stars](https://img.shields.io/github/stars/HelmholtzAI-Consultants-Munich/XAI-Tutorials?logo=GitHub&color=yellow)](https://github.com/HelmholtzAI-Consultants-Munich/XAI-Tutorials/stargazers)
 
-[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/HelmholtzAI-Consultants-Munich/XAI-Tutorials)
 
 # Tutorials for eXplainable Artificial Intelligence (XAI) methods
 
-This repository contains a collection of self-explanatory tutorials for different model-agnostic and model-specific XAI methods.
-Each tutorial comes in a Jupyter Notebook containing a short video lecture and practical exercises.
-The material has already been used in the context of two courses: the Zero to Hero Summer Academy (fully online) and ml4hearth (hybrid setting).
-The course material can be adjusted according to the available time frame and the schedule.
-The material is self-explanatory and can also be consumed offline.
+This repository contains a collection of self-explanatory tutorials for different model-agnostic and model-specific XAI methods for Random Forests, CNNs and Transformers. Each tutorial comes in a Jupyter Notebook containing a short video lecture and practical exercises.
 
 The learning objectives are:
 
@@ -19,21 +13,39 @@ The learning objectives are:
 - learn how to interpret the outputs and graphs of those methods with hands-on exercises
 - learn to choose which method is suitable for a specific task
 
-## List of Tutorials for Model-Agnostic Methods
+For using the content of this repository for an online or offline course, please open a new GitHub branch in this repository with the name of the course and then choose the content you would like to use for your course. The folders `docs`, `test` and `.github` can be removed in the course branch as they are only needed in the main branch.
+
+**List of Tutorials for Model-Agnostic Methods:**
 
 - Permutation Feature Importance
 - SHapley Additive exPlanations (SHAP)
 - Local Interpretable Model-Agnostic Explanations (LIME)
 
-## List of Tutorials for Model-Specific Methods
+**List of Tutorials for Model-Specific Methods:**
 
 - Forest-Guided Clustering
 - Grad-CAM
+- Attention Maps
 
 ## Requirements and Setup
 
 It is possible to either create an environment and install all the necessary packages locally (using the requirements.txt file) or to execute the notebooks on the browser, by clicking the 'Open in Colab' button. This second option doesn't require any further installation, but the user must have access to a Google account.
 
+If you prefer to run the notebooks on your device, create a virtual environment using the requirements.txt file:
+```
+conda create -n xai python=3.10
+conda activate xai
+pip install -r requirements.txt
+```
+
+Once your environment is created, clone the repo using the following command:
+
+```
+git clone https://github.com/HelmholtzAI-Consultants-Munich/XAI-Tutorials.git
+```
+
 ## Contributions
 
 Comments and input are very welcome! If you have a suggestion or think something should be changed, please open an issue or submit a pull request. 
+
+All content is publicly available under the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/