Yes, but just as with normal feature engineering, don't make the mistake of thinking that these methods are fully automatic work by magic. There is no free lunch.

A common criticism with these methods is that they merely shift engineering from features to parameters that specify the architecture. There are many choices to be made: The exact number of layers, number of neurons per layer, the connectivity, sparsity parameters, non-linearities, sizes of receptive fields, learning rates, weight decays, pre-training schedule etc etc etc. Perhaps even worse, while you can use intuition to design features, it is not as trivial to see if you should be using a sigmoid, tanh, or rectified linear units (+associated parameters for each) in the 3rd layer of the network. And maybe even worse, these parameters can actually have quite a strong effect on the final performance.

These are still powerful models and we are learning a lot about what works and what doesn't (and I'm optimistic) but don't make the mistake of thinking they are automatic. For now, you need to know what you're doing.