  We present a machine-learning approach for estimating galaxy cluster masses
from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a
deep machine learning tool commonly used in image recognition tasks. The CNN is
trained and tested on our sample of 7,896 Chandra X-ray mock observations,
which are based on 329 massive clusters from the IllustrisTNG simulation. Our
CNN learns from a low resolution spatial distribution of photon counts and does
not use spectral information. Despite our simplifying assumption to neglect
spectral information, the resulting mass values estimated by the CNN exhibit
small bias in comparison to the true masses of the simulated clusters (-0.02
dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our
best fold and 12% averaging over all. In contrast, a more standard core-excised
luminosity method achieves 15-18% scatter. We interpret the results with an
approach inspired by Google DeepDream and find that the CNN ignores the central
regions of clusters, which are known to have high scatter with mass.
