  Magnetic activity in stars manifests as dark spots on their surfaces that
modulate the brightness observed by telescopes. These light curves contain
important information on stellar rotation. However, the accurate estimation of
rotation periods is computationally expensive due to scarce ground truth
information, noisy data, and large parameter spaces that lead to degenerate
solutions. We harness the power of deep learning and successfully apply
Convolutional Neural Networks to regress stellar rotation periods from Kepler
light curves. Geometry-preserving time-series to image transformations of the
light curves serve as inputs to a ResNet-18 based architecture which is trained
through transfer learning. The McQuillan catalog of published rotation periods
is used as ansatz to groundtruth. We benchmark the performance of our method
against a random forest regressor, a 1D CNN, and the Auto-Correlation Function
(ACF) - the current standard to estimate rotation periods. Despite limiting our
input to fewer data points (1k), our model yields more accurate results and
runs 350 times faster than ACF runs on the same number of data points and
10,000 times faster than ACF runs on 65k data points. With only minimal feature
engineering our approach has impressive accuracy, motivating the application of
deep learning to regress stellar parameters on an even larger scale
