All true and necessary, but not sufficient. Or was there ever a #ML lib designed to *increase* overhead and make experiments difficult? :)
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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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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AFAIK, the champion of DSB2017 use PyTorch.
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