To do increasingly fancy things with feed forward neural networks, we require increasingly many layers. But with many layers, it becomes increasingly difficult to train it (vanishing gradient, explosive growth of connections, etc). But Hinton et al. found a few tricks to go past those obstacles and that's what 'deep learning' encompasses. Current state of the art of deep learning is at 150 odd layers with a new trick called 'residual learning' by Microsoft Research China.
I didn't say it was easy to do, if anything I was just taking a pot shot at the laziness of using the term as a label for all of modern machine learning.
I've been studying up on deep neural nets recently though and there are a bunch of very cool tricks (and I don't mean that pejoratively, I think real intelligence consists of just such a grab-bag of tricks), which make it effective in a wide range of cases.
