Deep learning is great if you're dealing with a hugely dimensional problem, and you have the data to train the model. If one of those things is not true (and usually, one of those things is not true), you're better off starting simple.
That's an excellent point and one that kept me from trying the deep learning approach for a long time. But in the end the machine + rudimentary deep learning provided its own dataset and that really made it work.
So even if I didn't have the data to train the model I had enough data to bootstrap the process and sometimes that's all you need.
Deep learning is great if you're dealing with a hugely dimensional problem, and you have the data to train the model. If one of those things is not true (and usually, one of those things is not true), you're better off starting simple.