  We propose a machine-learning-based technique to determine the number density
of radio sources as a function of their flux density, for use in
next-generation radio surveys. The method uses a convolutional neural network
trained on simulations of the radio sky to predict the number of sources in
several flux bins. To train the network, we adopt a supervised approach wherein
we simulate training data stemming from a large domain of possible number count
models going down to fluxes a factor of 100 below the threshold for source
detection. We test the model reconstruction capabilities as well as benchmark
the expected uncertainties in the model predictions, observing good performance
for fluxes down to a factor of ten below the threshold. This work demonstrates
that the capabilities of simple deep learning models for radio astronomy can be
useful tools for future surveys.
