Your deep learning models don't always work as expected. In such cases you need to debug it. Understanding how models works internally is required for debugging them.
I would wager that a lot of people who "do deep learning" have absolutely no idea about the models they're using.
Hyperparameter optimisation is basically a fudge right now - you try everything and see what works. Even the research groups who came up with the standard network stacks, like VGG, basically lucked out and found an architecture that worked, then tried several variants and found one that worked better. DL papers are full of handwaving speculation about why particular networks perform better than others, but right now it's just that: highly educated speculation.
This isn't limited to deep learning. If you want to try any kind of machine learning, it's totally reasonable to throw different fitting functions at your problem to see which one works best. Unless you have an unusually clear problem category, it's rarely possible to say at the outset that "This problem would best be solved with method <X>". A counter here would be that if you need to classify images, you should almost certainly use a convnet.
You need some understanding about why things might be going wrong, e.g. your loss isn't moving -> crank up the learning rate. You're seeing nans? Probably your learning rate is too high. But that doesn't really need any serious maths to understand. You can get by quite well by figuring out empirical rules.
I'm not arguing that you shouldn't learn the maths, it's a wise idea to, but many people use deep learning models without knowing how backpropagation works for instance.
