  All-sky photometric time-series missions have allowed for the monitoring of
thousands of young ($t_{\rm age} < 800$Myr) to understand the evolution of
stellar activity. Here we developed a convolutional neural network (CNN),
$\texttt{stella}$, specifically trained to find flares in $\textit{Transiting
Exoplanet Survey Satellite}$ ($\textit{TESS}$) short-cadence data. We applied
the network to 3200 young stars to evaluate flare rates as a function of age
and spectral type. The CNN takes a few seconds to identify flares on a single
light curve. We also measured rotation periods for 1500 of our targets and find
that flares of all amplitudes are present across all spot phases, suggesting
high spot coverage across the entire surface. Additionally, flare rates and
amplitudes decrease for stars $t_{\rm age} > 50$Myr across all temperatures
$T_{\rm eff} \geq 4000$K, while stars from $2300 \leq T_{\rm eff} < 4000$K show
no evolution across 800 Myr. Stars of $T_{\rm eff} \leq 4000$K also show higher
flare rates and amplitudes across all ages. We investigate the effects of high
flare rates on photoevaporative atmospheric mass loss for young planets. In the
presence of flares, planets lose 4-7% more atmosphere over the first 1 Gyr.
$\texttt{stella}$ is an open-source Python tool-kit hosted on GitHub and PyPI.
