Debugging ML models (large part of my job) requires very little math. Engineering experience and mindset is a lot more relevant for debugging. Complicated math is typically needed when you want invent new loss functions, or new methods for regularization, normalization or model compression.

▲ srean 7 hours ago | parent [-]

You are perhaps talking about some simple plumbing bugs. There are other kinds:

Why didn't the training converge

Validation/test errors are great but why is performance in the wild so poor

Why is the model converging so soon

Why is this all zero

Why is this NaN

Model performance is not great, do I need to move to something more complicated or am I doing something wrong

Did the nature of the upstream data change ?

Sometimes this feature is missing, how should I deal with this

The training set and the data on which the model will be deployed are different. How to address this problem

The labelers labelled only the instances that are easy to label, not chosen uniformly from the data. How to train with such skewed label selection

I need to update model but with a few thousand data points but not train from scratch. How do I do it

Model too large which doubles can I replace with float32

So on and so forth. Many times models are given up on prematurely because the expertise to investigate lackluster performance does not exist in the team.

▲ p1esk 5 hours ago | parent [-]

Literally every single example you provided does not require much math fundamentals. Just basic ML engineering knowledge. Are you saying that understanding things like numerical overflow or exploding gradients require sophisticated math background?

▲ srean 3 hours ago | parent [-]

Numerical overflow mostly no, but in case of exploding gradient, yes especially about coming up with a way to handle it, on your own, from scratch. After all, it took the research community some time to figure out a fix for that.

But the examples you quoted were not my examples, at least not their primary movers (the NaNs could be caused by overflow but that overflow can have a deeper cause). The examples I gave have/had very different root causes at play and the fixes required some facility with maths, not to the extent that you have to be capable of discovering new math, or something so complicated as the geometry and topology of strings, but nonetheless math that requires grad school or advanced and gifted undergrad level math.

Coming back to numeric overflow that you mention. I can imagine a software engineer eventually figuring out that overflow was a root cause (sometimes they will not). However there's quite a gap between overflow recognition and say knowledge of numerical analysis that will help guide a fix.

You say > "literally every single example"... can be dealt without much math. I would be very keen to learn from you about how to deal with this one, say. Without much math.

   The labelers labelled only
   the instances that are
   easy to label, not chosen
   uniformly from the data.
   How to train with such
   skewed label selection 
   (without relabeling properly)

This is not a gotcha, a genuine curiosity here because it is always useful to understand a solution different from your own(mine).

▲

p1esk an hour ago | parent [-]

Maybe I don’t understand this data labeling issue - are you talking about imbalanced classification dataset? Are hard classes under-represented or missing labels completely?

	▲	srean an hour ago \| parent [-]
		None of those (but they could be added to the mix to complicate matters). Consider the case that the labelers creates the labelled training set by cherry picking those examples that are easy to label. He labels many, but selects the items to label according to his preference. First question, is this even a problem. Yes, most likely. But why ? How to fix it ? When are such fixes even possible.