Theory and implementation are often just poles apart. It is important to understand the theory. But it is even more important to understand how to put it into practice. For that, we need an idea about the kind of issues involved in solving real world problems and the typical solutions employed.
It is wise not to waste time in solving all problems right from the first principles. There are times when we must question these basics and try to think differently, but often it is wiser to start with these established best practices and see how things work.
Machine Learning experts have defined several such best practices. A lot of them may seem intuitive and trivial. Others seem to be nerdy. But, as we said, it is important to have these in mind when we start. That helps us focus on the real problem rather than the peripherals.