Concept-based Interpretable Deep Learning in Python

This book demonstrates how to build interpretable deep learning models in Python.

Introduction to Concept-based Interpretability

• 1. The tale of the deep learning model that failed my driving exam
• 2. Concept Bottleneck Models
• 3. Quantitative metrics in concept interpretability

• Bibliography

• PyC Team

Cite this book

If you found this book useful for your blog post, research article or product, we would be grateful if you would cite it like this:

Barbiero P., Ciravegna G., Debot D., Diligenti M., 
Dominici G., Espinosa Zarlenga M., Giannini F., Marra G. (2024).
Concept-based Interpretable Deep Learning in Python.
https://pyc-team.github.io/pyc-book/intro.html

Or use the following bibtex entry:

@book{pycteam2024concept,
  title      = {Concept-based Interpretable Deep Learning in Python},
  author     = {Pietro Barbiero, Gabriele Ciravegna, David Debot, Michelangelo Diligenti, Gabriele Dominici, Mateo Espinosa Zarlenga, Francesco Giannini, Giuseppe Marra},
  year       = {2024},
  url        = {https://pyc-team.github.io/pyc-book/intro.html}
}