  Stellar flares are an important aspect of magnetic activity -- both for
stellar evolution and circumstellar habitability viewpoints - but automatically
and accurately finding them is still a challenge to researchers in the Big Data
era of astronomy. We present an experiment to detect flares in space-borne
photometric data using deep neural networks. Using a set of artificial data and
real photometric data we trained a set of neural networks, and found that the
best performing architectures were the recurrent neural networks (RNNs) using
Long Short-Term Memory (LSTM) layers. The best trained network detected flares
over {$5\sigma$ with $\gtrsim80$\% recall and precision} and was also capable
to distinguish typical false signals (e.g. maxima of RR Lyr stars) from real
flares. Testing the network trained on Kepler data on TESS light curves showed
that the neural net is able to generalize and find flares - with similar
effectiveness - in completely new data having previously unseen sampling and
characteristics.
