Deep learning is especially overhyped by articles that describe it as "just like the human brain". It's a useful technique that lets you skip lots of feature engineering by just letting the classifier learn its own features, but it is also really easy to project abilities onto neural networks that they don't really have.

Deep learning is not magic, for every network architecture that beats the state of the art, there are a hundred very similar ones that completely fail, or run too slow, or don't fit into GPU memory ... and the only way we know to get improvements is to fiddle with the hyperparameters until everything works out.