Because deep learning is booming, the field lacks best practices; practitioners tend to be new. Combined with hype, this is a bad situation
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Two problems that will arise are credit assignment and conflicts of interest (pushing one paper over another). Classic academic problems
I would start such a project with topics that are not typically the subject of papers (hence information harder to come across)
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e.g. best practices for data cleaning / tokenization / vectorization, optimization / training, parameter tuning, regularization
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the sort of knowledge that people tend to build from experience rather than from reading papers.
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