Keras has been reliably winning almost every deep learning competition on Kaggle. Here are some thoughts as to why: https://www.quora.com/Why-has-Keras-been-so-successful-lately-at-Kaggle-competitions/answer/Fran%C3%A7ois-Chollet …
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All true and necessary, but not sufficient. Or was there ever a
#ML lib designed to *increase* overhead and make experiments difficult? :)
Not deliberately, but in practice, yes, absolutely. Designing a library to reduce cognitive load is still the exception, not the rule.
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I can't think of any that wouldn't have "ease of use" as a primary objective. Are we talking
#sklearn wrt Kaggle? Or which ones? -
Sklearn's defining characteristic was precisely ease of use. It stood out. *Most* specialized software libs aren't trying to be easy to use
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