  Machine learning techniques have been successfully applied to classifying an
extensive range of phenomena in quantum theory. From detecting quantum phase
transitions to identifying Bell non-locality, it has been established that
classical machines can learn genuine quantum features via classical data.
Quantum entanglement is one of the uniquely quantum phenomena in that range, as
it has been shown that neural networks can be used to classify different types
of entanglement. Our work builds on this topic. We investigate whether distinct
models of neural networks can learn how to detect catalysis and self-catalysis
of entanglement. Additionally, we also study whether a trained machine can
detect another related phenomenon - which we dub transfer knowledge. As we
build our models from scratch, besides making all the codes available, we can
study a whole gamut of paradigmatic measures, including accuracy, execution
time, training time, bias in the training data set and so on.
