Maybe this is more of a general ML question but I faced it when transformers became popular. Do you know of a project-based tutorial that talks more about neural net architecture, hyperparameters selection and debugging? Something that walks through getting poor results and make explicit the reasoning for tweaking?
When I try to use transformers or any AI thing on a toy problem I come up with, it never works. And there's this blackbox of training that's hard to debug into. Yes, for the available resources, if you pick the exact problem, the exact NN architecture and exact hyperparameters, it all works out. But surely they didn't get that on the first try. So what's the tweaking process?
When I try to use transformers or any AI thing on a toy problem I come up with, it never works. And there's this blackbox of training that's hard to debug into. Yes, for the available resources, if you pick the exact problem, the exact NN architecture and exact hyperparameters, it all works out. But surely they didn't get that on the first try. So what's the tweaking process?