  We use a multilevel perceptron (MLP) neural network to obtain photometry of
saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The
MLP can obtain fairly unbiased photometry for stars from g~4 to 14~mag,
particularly compared to the dispersion (15%-85% 1sigma range around the
median) of 0.12 mag for saturated (g<11.5 mag) stars. More importantly, the
light curve of a non-variable saturated star has a median dispersion of only
0.037 mag. The MLP light curves are, in many cases, spectacularly better than
those provided by the standard ASAS-SN pipelines. While the network was trained
on g band data from only one of ASAS-SN's 20 cameras, initial experiments
suggest that it can be used for any camera and the older ASAS-SN V band data as
well. The dominant problems seem to be associated with correctable issues in
the ASAS-SN data reduction pipeline for saturated stars more than the MLP
itself. The method is publicly available as a light curve option on ASAS-SN Sky
Patrol v1.0.
