This is something I've also struggled with. I find it hard to read deep learning papers because I need to translate each math notation, thus struggling to get the bigger picture. I'm fond of the bottom-up approach, e.g. I started by mastering C and wrote my own libraries. But for deep learning I lean towards the opposite, starting with high-level libraries. When I want to understand the theory I search for simple python code that I can implement from scratch. This way I can understand the logic, without having to understand all the math behind it. I've mostly focused on doing Kaggle type of problems and used MOOCs when I get stuck. I've had little interest from larger companies, but I've managed to get a few offers from startups. Startups often have a couple of people with PhD-level knowledge but are also looking programmers that can code the models.