  In this article, we explore the potential of artificial neural networks,
which are trained using an exceptionally simplified catalog of ideal
configurations encompassing both order and disorder. We explore the
generalisation power of these networks to classify phases in complex models
that are far from the simplified training context. As a paradigmatic case, we
analyse the order-disorder transition of the diluted Ising model on several
two-dimensional crystalline lattices, which does not have an exact solution and
presents challenges for most of the available analytical and numerical
techniques. Quantitative agreement is obtained in the determination of
transition temperatures and percolation densities, with comparatively much more
expensive methods. These findings highlight the potential of minimalist
training in neural networks to describe complex phenomena and have implications
beyond condensed matter physics.
