[D] Good ML Eng question banks for interviews?

I've been studying for ML engineering interviews (and doing some), and I've realized that the common advice of "learn about bias, variance, cross-fold validation, etc." is all wrong. The top companies are asking you to code simple things using Pytorch/numpy. So questions are things like: "write a neural net to solve X problem" or "implement k-means using numpy".

Given this is the case, I think it's much more useful to prepare for these interviews by doing a bunch of coding …

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