algorithm and I would say that still qualifies as deep learning and then in terms of models that aren't necessarily differentiable I guess Boltzmann machines are probably the main example of something where you can't really take a derivative and use that for the learning process but you you can still argue that the model has many steps of processing that it applies when you run inference in the model so that's the steps of processing that's key so geoff hinton suggests that we need to throw away back prop back