In this thesis, a new family of high-level jet flavour tagging algorithms called DL1 is presented. It is now established within the ATLAS collaboration at the Large Hadron Collider at CERN to be applied to Run 2 pp collision data at √s = 13 TeV. DL1 represents the first use of Deep Learning for ATLAS physics object reconstruction as well as the first major application of advanced deep neural networks within the collaboration. The development and structure of DL1 are described and a detailed set of performance plots is presented. The determination of jets originating from heavy flavour quarks is used to probe the particle identity of particles created in the pp collisions. These heavy flavour quarks play a major role in searches for new physics and precision measurements. DL1 is expected to improve a wide range of physics analyses throughout the collaboration.