  We present a neural network for the solution of the inverse swarm problem of
deriving cross sections from swarm transport data. To account for the
uncertainty inherent to this somewhat ill-posed inverse problem, we train the
neural network using cross sections from the LXCat project, paired with
associated transport coefficients found by the numerical solution of
Boltzmann's equation. The use of experimentally measured and theoretically
calculated cross sections for training encourages the network to avoid
unphysical solutions, such as those containing spurious energy-dependent
oscillations. We successfully apply this machine learning approach to simulated
swarm data for electron transport in helium, separately determining its elastic
momentum transfer and ionisation cross sections to within an accuracy of $4\%$
over the range of energies considered. Our attempt to extend our method to
argon was less successful, although the reason for that observation is
well-understood. Finally, we explore the feasibility of simultaneously
determining cross sections of helium using this approach. We have some success
here, determining elastic, total $n=2$ excitation and ionisation cross sections
to $10\%$, $20\%$ and $25\%$ accuracy, respectively. We are unsuccessful in
properly unfolding the separate $n=2$ singlet and triplet excitation cross
sections of helium, but this is as expected given their similar threshold
energies.
