How to Look at Common Machine Learning Tasks with Fresh Eyes

We’d never recommend changing robust, well-performing workflows just for the sake of change; “if it ain’t broke, don’t fix it” is a common folksy idiom for a reason: it’s very often the correct approach.

Still, there’s a sizeable gap between “very often” and “always,” and our most frustrating days at work typically come about when our time-tested methods fail to produce our expected outcomes or perform poorly. This is where expanding our knowledge base really pays off: instead of getting …

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