  A versatile magnetometer must deliver a readable response when exposed to
target fields in a wide range of parameters. In this work, we experimentally
demonstrate that the combination of $^{171}$Yb$^{+}$ atomic sensors with
adequately trained neural networks enables to investigate target fields in
distinct challenging scenarios. In particular, we characterize radio frequency
(RF) fields in the presence of large shot noise, including the limit case of
continuous data acquisition via single-shot measurements. Furthermore, by
incorporating neural networks we significantly extend the working regime of
atomic magnetometers into scenarios in which the RF driving induces responses
beyond their standard harmonic behavior. Our results indicate the benefits to
integrate neural networks at the data processing stage of general quantum
sensing tasks to decipher the information contained in the sensor responses.
