Now-a-days, literature in deep learning is increasing at a rapid rate.
One of the key difference with other domains is that almost all research papers in deep learning provide corresponding code in GitHub.
It is (relatively) easy for me to understand the (mathematical, analytical aspects of) research paper. But, I personally feel it difficult to understand existing codes.
in this context, I want to know the common practice among researchers regarding the existing codes. Do researchers in deep learning understand the existing codes in detail? Or they just use the code as a module to execute?
Is there any recommended approach towards complex existing codes?