  We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy
strong gravitational lenses using convolutional neural networks. We generate
250,000 simulated lenses at redshifts > 0.8 from which we create a data set for
training the neural networks with realistic seeing, sky and shot noise. Using
the simulations as a guide, we build a catalogue of 1.1 million DES sources
with 1.8 < g - i < 5, 0.6 < g -r < 3, r_mag > 19, g_mag > 20 and i_mag > 18.2.
We train two ensembles of neural networks on training sets consisting of
simulated lenses, simulated non-lenses, and real sources. We use the neural
networks to score images of each of the sources in our catalogue with a value
from 0 to 1, and select those with scores greater than a chosen threshold for
visual inspection, resulting in a candidate set of 7,301 galaxies. During
visual inspection we rate 84 as "probably" or "definitely" lenses. Four of
these are previously known lenses or lens candidates. We inspect a further
9,428 candidates with a different score threshold, and identify four new
candidates. We present 84 new strong lens candidates, selected after a few
hours of visual inspection by astronomers. This catalogue contains a comparable
number of high-redshift lenses to that predicted by simulations. Based on
simulations we estimate our sample to contain most discoverable lenses in this
imaging and at this redshift range.
