No. Deep learning is a form of representation learning, where the features of the model are automatically discovered from the data instead of manually constructed, and how to do this is a central problem in machine learning. But there are other approaches to representation learning, like inducing hidden variables in graphical models and predicate invention in symbolic learning, that can do things current deep learning algorithms can't.
Of course, I can imagine starting from deep learning and gradually extending it to incorporate these capabilities, and to some extent this is already happening, but you can equally well start from the other direction, and indeed that's happening as well. Either way, the end result is not pure deep learning (although given the current enthusiasm around it, some people might mistake it for such!).
Beyond that, there are other important issues in machine learning besides representation learning, such as learning from delayed rewards, which is the focus of reinforcement learning, which deep learning per se does not address. Again, what we see here is combinations of deep learning with other types (e.g., Q-learning in DeepMind's Atari player). And backpropagation, which is what powers most deep learning systems, solves the credit assignment problem, but it doesn't solve other crucial problems, like learning structure, learning composable knowledge, generalizing out of sample, etc. So we need lots more besides deep learning to have a truly general-purpose learner.
Answered by-Pedro Domingos
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