Ask HN: Knowledge gap between machine learning research and a functioning model?
I have been asked to implement an unkown machine learning algorithm that has been researched and proven by a team from a top University.
The goal is to build a functioning machine learning model and a series of software products around it.
For anyone with experience in this type of academic research or the software implementation of it, how much actual knowledge of machine learning should I have before attempting this?
Should I expect the research algorithms to work with minimal changes?
Or should I expect to fully rewrite the model using the research as a rough guide instead?
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