[raverbashing]

> But I highly encourage you to read the winners' solutions. They are full of clever data insight, augmentations, regularizations, feature engineering, and preprocessing and postprocessing tricks.

> But above all, compared to the academic literature, it's shocking how much time and creativity they spend on validation. Maybe I'm reading the wrong papers, but the flashy new neural architectures rarely even mention their validation setup; Kaggle winners sometimes devote half of their explanation to it

I agree, but in the end it is a competition, and the solution that scores the most is not always the solution that is "the most interesting" (or practical, or best in real world cases)

Though the details you mention are interesting, and can definitely apply at real-life solutions.