  We present a real-time stamp classifier of astronomical events for the ALeRCE
(Automatic Learning for the Rapid Classification of Events) broker. The
classifier is based on a convolutional neural network, trained on alerts
ingested from the Zwicky Transient Facility (ZTF). Using only the
\textit{science, reference} and \textit{difference} images of the first
detection as inputs, along with the metadata of the alert as features, the
classifier is able to correctly classify alerts from active galactic nuclei,
supernovae (SNe), variable stars, asteroids and bogus classes, with high
accuracy ($\sim$94\%) in a balanced test set. In order to find and analyze SN
candidates selected by our classifier from the ZTF alert stream, we designed
and deployed a visualization tool called SN Hunter, where relevant information
about each possible SN is displayed for the experts to choose among candidates
to report to the Transient Name Server database. From June 26th 2019 to
February 28th 2021, we have reported 6846 SN candidates to date (11.8
candidates per day on average), of which 971 have been confirmed
spectroscopically. Our ability to report objects using only a single detection
means that 70\% of the reported SNe occurred within one day after the first
detection. ALeRCE has only reported candidates not otherwise detected or
selected by other groups, therefore adding new early transients to the bulk of
objects available for early follow-up. Our work represents an important
milestone toward rapid alert classifications with the next generation of large
etendue telescopes, such as the Vera C. Rubin Observatory.
