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Am I the only one who feels like all that math is required in order to properly implement an ML system? Bugs in ML systems are insidious and tricky, often diffi
by nharada 10y ago
Am I the only one who feels like all that math is required in order to properly implement an ML system? Bugs in ML systems are insidious and tricky, often difficult to find and manifesting in unexpected ways based on subtle issues in the underlying data or mathematics. Having a ground up understanding of why things work the way they do is a requirement to reasoning through all the layers of the system to find and fix issues.
Maybe someday all of that will be abstracted enough that you don't need to know math to do ML (like how now you generally don't need to know machine architecture to program), but how soon will that be?
- tjl 10y agoIt might be some time before it's abstracted enough. For example, there's software packages for Finite Element Analysis, but if you don't know the underlying math, you can't do anything more complicated than the basics. Plus, if there's a problem without that knowledge you can't really debug it. I'm guessing ML will be like that for a while. If you want to do something simple using a package is fine, but as soon as something goes wrong you'll need that knowledge to figure out what's happening. For example, for a while there's been work on doing sentiment analysis using machine learning and they typically train them on a data set of movie reviews. It turns out that as soon as you apply that trained system to anything other than movie reviews the actual results are quite poor, but you might not catch it.