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by lacampbell
3443 days ago
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and those who comment arguing the relevance of "real math" in the era of computers. Is this related to my comment? I used "age of computers", but close enough. It's really not a fair representation of what I said at all. I stressed the importance of knowing theorems and deriving proofs - arguably "realer" math than learning an equation by rote. I did some applied maths in undergrad, and in my experience a lot of my time was devoted to solving large and complex equations using fairly mechanical rules, and comparatively little of my time was spent on axioms and proofs. I wonder whether this focus is justified in the age of computers - might we derive the complex formulas just once or twice as an exercise, and not step through them ourselves again and again? Might we focus more on what the computer can't do well for us - rigour and intuition? |
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It was initially related, yeah, but I realized I had uncharitably read your point. I edited my comment, but not enough. Sorry about that.
To be fair, this point is often raised in these threads as "why do math when computers do it for us?" so the criticism wasn't specifically levied against you.
We agree that repeated derivation when working on a new problem can be useless. It would be silly to work out OLS assumptions from first principles upon any import of sklearn.linear_model! I believe understanding those assumptions, though, or (say) how backpropagation works is important, since (1) it can help you debug issues and (2) explain modifications to the core models (GLMs or LSTMs, in the above examples).