I will survey the status of Deep Learning in High Energy Physics, with focus on new emerging techniques. I will discuss the path from the current state of mostly proof of principle and feasibility studies to physics results incorporating the new techniques and eventually incorporating Deep Learning into the production workflow of HEP experiments. And I will propose future Deep Learning projects in HEP.