  The flavour-tagging algorithms developed by the ATLAS Collaboration and used
to analyse its dataset of $\sqrt s = 13$ TeV $pp$ collisions from Run 2 of the
Large Hadron Collider are presented. These new tagging algorithms are based on
recurrent and deep neural networks, and their performance is evaluated in
simulated collision events. These developments yield considerable improvements
over previous jet-flavour identification strategies. At the 77% $b$-jet
identification efficiency operating point, light-jet (charm-jet) rejection
factors of 170 (5) are achieved in a sample of simulated Standard Model
$t\bar{t}$ events; similarly, at a $c$-jet identification efficiency of 30%, a
light-jet ($b$-jet) rejection factor of 70 (9) is obtained.
