IDE: JupyterLab
- Built-in functions: https://docs.python.org/3.11/library/functions.html
- Code Style: https://docs.python-guide.org/writing/style/?highlight=functions
- Structure of the Repository : https://docs.python-guide.org/writing/structure/
- Classes: https://docs.python.org/3/tutorial/classes.html
- Built-in Exceptions: https://docs.python.org/3.11/library/exceptions.html
- Matplotlib: https://matplotlib.org/
- sklearn.datasets: https://scikit-learn.org/stable/datasets.html#datasets
- Iris-Data-Walkthrough: https://github.com/jewelbritton/Iris-Data-Walkthrough
- Multiclass and multioutput algorithms: https://scikit-learn.org/stable/modules/multiclass.html
- sklearn.datasets: https://scikit-learn.org/stable/datasets.html#datasets
- Iris-Data-Walkthrough: https://github.com/jewelbritton/Iris-Data-Walkthrough
- sklearn.preprocessing: https://scikit-learn.org/stable/api/sklearn.preprocessing.html
- TechDispatch #2/2023 - Explainable Artificial Intelligence: https://www.edps.europa.eu/system/files/2023-11/23-11-16_techdispatch_xai_en.pdf
- EU Artificial Intelligence Act: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=OJ:L_202401689
- Explainable AI lacks regulative reasons: why AI and human decision‑making are not equally opaque: https://doi.org/10.1007/s43681-022-00217-w
- Interpretable AI: Building explainable machine learning systems: https://ieeexplore.ieee.org/document/10280544
- DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation: https://doi.org/10.48550/arXiv.1806.10313
- AI4PV - D3.1: Models for root-cause analysis with data analytics: https://ai4pv.eu/wp-content/uploads/AI4PV-D3.1-Final.pdf