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The seems to be a tacit belief in the ML community that you should include at least a few equations for your paper to be considered rigorous. Math notation as a cargo-cult token of rigor. But math, like figures, like English, is just a language to express and develop ideas.
You can think rigorously without mathematical notation. Inversely, some math-heavy papers use math to obfuscate a lack of rigor -- leveraging the fact that it's harder to read for most, and that it confers an appearance of scientificness.
Using math is like the choice of a programming language: for some classes of problems, it can make you a lot more productive. You can go a very long way without math (maybe all the way, if you have code instead). But if you master math, that's a key advantage you should leverage
Being highly mathematical is surprisingly rare in the ML community. At this point, it's almost like a superpower. Use math to think, not to impress
what's "ML"?
Machine Learning
May I respectfully beg to differ that "...math, like figures, like English is *just* a language to express and develop ideas."
Why was calculus invented?
Did Goddard understand mathematics to be "just a language?"
cc @NASApic.twitter.com/ezVgvKPUkF
Math is not 'a language'. Mathematics is much more than the set of symbols somebody uses to express it. It's the science of rigorous understanding and sound reasoning. You can write mathematics in plain english.
Just takes waaaaaaay longer.
I find the math useful, especially if the algorithms are poorly documented in pseudo code.
R U kidding me?
he's serious
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