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.
And parameter selection isn't nearly as involved in practice--but it is important to know what you're doing because you'll basically be translating from papers to code in order to get a working implementation, at least until some startup comes out with a plug and play deep learning paas
For anyone interested in playing with the tools that are available, deeplearning.net provides a set of very good tutorials with working code.
