The author points out something about these Machine Learning contests and Machine Learning in general that I've noticed for a while - feature selection tends to dominate learning algorithm selection. It's good to see that there are modern academic methods for feature discovery that seem to be on par with (or better than) a domain expert manually selecting features.
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 par…
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.