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Practical work versus theory in a move to machine learning
After years in academia, a practitioner says they focused on getting things to work in ML instead of learning theory.
TLDR
While moving into machine learning after years in academia, the poster says they chose to focus on getting things to work rather than learning theory. They had planned to fill that gap someday, but later concluded that doing so would largely be a waste of time.
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