Deep learning is much different from typical programming topics in that it is composed of a large amount of mathematical and complex theoretical concepts that are not avoidable using a library. Getting the code to run is relatively easy, and the choice of library has mostly to do with deployment goals and utilizing existing implementations. When things aren't working, theres not really a compiler error as to what's wrong with the model/data pipeline, and deep theoretical knowledge comes into play.