hyperparameter tuning

Grid and random search ignore past results. Bayesian optimization learns from them, and the signal is whether you can explain the surrogate plus acquisition loop and name the exact condition where the sample-efficiency is worth it. Here is the answer.

Grid search is the wrong answer at scale. The signal is knowing why random beats grid, how Bayesian optimization and early-stopping schemes (Hyperband/ASHA) spend compute smartly, and how to parallelize without stragglers.