  Supermassive black holes (SMBHs) are ubiquitously found at the centers of
most massive galaxies. Measuring SMBH mass is important for understanding the
origin and evolution of SMBHs. However, traditional methods require
spectroscopic data which is expensive to gather. We present an algorithm that
weighs SMBHs using quasar light time series, circumventing the need for
expensive spectra. We train, validate, and test neural networks that directly
learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 light curves for a
sample of $38,939$ spectroscopically confirmed quasars to map out the nonlinear
encoding between SMBH mass and multi-color optical light curves. We find a
1$\sigma$ scatter of 0.37 dex between the predicted SMBH mass and the fiducial
virial mass estimate based on SDSS single-epoch spectra, which is comparable to
the systematic uncertainty in the virial mass estimate. Our results have direct
implications for more efficient applications with future observations from the
Vera C. Rubin Observatory. Our code, \textsf{AGNet}, is publicly available at
\url{https://github.com/snehjp2/AGNet}.
