A step which was taken a long time ago and does not seem to have played much of a role in recent developments; for the most part, people don’t bother with extensive hyperparameter tuning. Better initialization, better algorithms like dropout or residual learning, better architectures, but not hyperparameters.
The key point is that machine learning starts to happen at the hyper-parameter level. Which is one more step toward systems that optimize themselves.
A step which was taken a long time ago and does not seem to have played much of a role in recent developments; for the most part, people don’t bother with extensive hyperparameter tuning. Better initialization, better algorithms like dropout or residual learning, better architectures, but not hyperparameters.