  We use physics-informed neural networks (PINNs) to compute the first
quasi-normal modes of the Kerr geometry via the Teukolsky equation. This
technique allows us to extract the complex frequencies and separation constants
of the equation without the need for sophisticated numerical techniques, and
with an almost immediate implementation under the \texttt{PyTorch} framework.
We are able to compute the oscillation frequencies and damping times for
arbitrary black hole spins and masses, with accuracy typically below the
percentual level as compared to the accepted values in the literature. We find
that PINN-computed quasi-normal modes are indistinguishable from those obtained
through existing methods at signal-to-noise ratios (SNRs) larger than 100,
making the former reliable for gravitational-wave data analysis in the mid
term, before the arrival of third-generation detectors like LISA or the
Einstein Telescope, where SNRs of ${\cal O}(1000)$ might be achieved.
