  We present a deep machine learning algorithm to extract crystal field (CF)
Stevens parameters from thermodynamic data of rare-earth magnetic materials.
The algorithm employs a two-dimensional convolutional neural network (CNN) that
is trained on magnetization, magnetic susceptibility and specific heat data
that is calculated theoretically within the single-ion approximation and
further processed using a standard wavelet transformation. We apply the method
to crystal fields of cubic, hexagonal and tetragonal symmetry and for both
integer and half-integer total angular momentum values $J$ of the ground state
multiplet. We evaluate its performance on both theoretically generated
synthetic and previously published experimental data on CeAgSb$_2$, PrAgSb$_2$
and PrMg$_2$Cu$_9$, and find that it can reliably and accurately extract the CF
parameters for all site symmetries and values of $J$ considered. This
demonstrates that CNNs provide an unbiased approach to extracting CF parameters
that avoids tedious multi-parameter fitting procedures.
