  The separation of $b$-quark initiated jets from those coming from lighter
quark flavors ($b$-tagging) is a fundamental tool for the ATLAS physics program
at the CERN Large Hadron Collider. The most powerful $b$-tagging algorithms
combine information from low-level taggers, exploiting reconstructed track and
vertex information, into machine learning classifiers. The potential of modern
deep learning techniques is explored using simulated events, and compared to
that achievable from more traditional classifiers such as boosted decision
trees.
