3 ms·
Huh? You use the theory to figure out what to implement in practice. The idea of just randomly hacking some shit together then backfitting a theory onto it is
by jey 16y ago
Huh? You use the theory to figure out what to implement in practice.
The idea of just randomly hacking some shit together then backfitting a theory onto it is absurd. That's the same strategy that led to many of the past failures of "AI" -- approaches based too much on intuition that wasn't theoretically well grounded.
Probability theory and statistical models are foundational material for anyone interested in machine learning.
- madmanslitany 16y agoThere's a great applicable quote from Nikola Tesla here: “If Edison had a needle to find in a haystack, he would proceed at once with the diligence of the bee to examine straw after straw until he found the object of his search. I was a sorry witness of such doings, knowing that a little theory and calculation would have saved him ninety per cent of his labor.”
- gjm11 16y agoAs I just pointed out to someone else who quoted the exact same thing elsewhere on HN, that approach seemed to work out pretty well for Edison.
- moultano 16y agoI don't agree with the parent, but I also don't think backfitting the theory is absurd. What I've discovered from using machine learning in practice is that it's far more important to degrade gracefully when you have little data than to do the theoretically best thing when you have a lot of data. What this ends up meaning is that a hacky thing that is somewhat reasonable but based on the realities of the data will usually perform better than something more sophisticated that made too many simplifying assumptions along the way. (That said, stats is amazing, and is the most important thing to learn for anyone getting into machine learning.)