  Recently proposed spintronic devices use magnetic skyrmions as bits of
information. The reliable detection of those chiral magnetic objects is an
indispensable requirement. Yet, the high mobility of magnetic skyrmions leads
to their stochastic motion at finite temperatures, which hinders the precise
measurement of the topological numbers. Here, we demonstrate the successful
training of artificial neural networks to reconstruct the skyrmion number in
confined geometries from time-integrated, dimensionally reduced data. Our
results prove the possibility to recover the topological charge from a
time-averaged measurement and hence smeared dynamic skyrmion ensemble, which is
of immediate relevance to the interpretation of experimental results,
skyrmion-based computing, and memory concepts
