I did my PhD thesis on a machine learning problem. I initially used deep learning but after a while I became frustrated with how opaque it was so I switched to using a graphical model where I had explicitly defined the variables and their statistical relationships. My new model worked but it required several months of trying out different models and tweaking parameters, not to mention a whole lot of programming things from scratch. Deep learning is opaque but it has the advantage that you can get good results rapidly without thinking a lot about the problem. That's probably the main reason that it's used.