I think you should apply ML deeply to a domain you care about, but see if you can find a domain that can be both generative as well as for understanding. If you are heavy into the math and don't need a grounding basis, maybe you don't need a domain to apply the ML research to, but the best scientists had a problem they were trying to solve, not just "doing research". Basically research in strong direction, for strong purpose solving a problem.
I guessed you asked a low level mechanical question. How do I get from A to B. You might already have the domain.
So to answer the actual question, I'd pick something like MNIST (digit recognition problem) and master it by hand from scratch using multiple techniques, as many techniques as I could find. So that I am applying each algorithm to a fixed problem, so that the algorithm and then later a paper the algorithm gets embedded in my mind.
Use only cleaned datasets, spend zero energy on those a the beginning. Cleaning is a separate job and two different things don't need to be learned here. In fact stick with only industry benchmark data so you can compare your results to more papers.
> ML/DL research
I think you should apply ML deeply to a domain you care about, but see if you can find a domain that can be both generative as well as for understanding. If you are heavy into the math and don't need a grounding basis, maybe you don't need a domain to apply the ML research to, but the best scientists had a problem they were trying to solve, not just "doing research". Basically research in strong direction, for strong purpose solving a problem.
I guessed you asked a low level mechanical question. How do I get from A to B. You might already have the domain.
So to answer the actual question, I'd pick something like MNIST (digit recognition problem) and master it by hand from scratch using multiple techniques, as many techniques as I could find. So that I am applying each algorithm to a fixed problem, so that the algorithm and then later a paper the algorithm gets embedded in my mind.
Use only cleaned datasets, spend zero energy on those a the beginning. Cleaning is a separate job and two different things don't need to be learned here. In fact stick with only industry benchmark data so you can compare your results to more papers.