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Comment by ic_fly2 | Hacker News Reader

1) depends, I find ML much more interesting than software engineering.

2) Not me

3) With difficulty, but through courses like fast.ai

If you’re not into maths and problem solving this is probably the wrong path. The main value add you bring is being able to transform a business problem into solvable maths.

Read Introduction to statistical learning and look at the fast.ai courses. I also recommend the paper attention is all you need. If you find all of those things interesting and not too mathsy then I’d say go for the switch. Else, maybe look at options in data engineering or as a software engineer in a ML team.

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When people talk about working with ML requiring a lot of math, what do they mean? That is to say, I have a degree in electrical engineering so I learned a lot of math. However, my programming career hasn't required me to actually use it since I graduated.

So I understand/"know" a lot of math. However, it would be tough for me to build back up to the point where I can, for example, solve differential equations again.

In a manner of speaking: Does a career in ML require a strong understanding/knowledge of math or does it require you to be able to solve a lot of math?

> Does a career in ML require a strong understanding/knowledge of math or does it require you to be able to solve a lot of math?

No. With ANNs, unless you're doing research into totally new ML architectures, you're not gonna do any maths apart from some arithmetic. And even if you do, it's usually quite simple maths, mostly matrix multiplications and simple non-linear transformations of scalars.

Nothing even close to solving differential equations, and there is very little analytical solving of anything. Advanced stuff may need some statistics in the theory side, but not really in applications.

Just to he sure that I understand the context, are you working as MLE or where from you had that knowledge?

No. I use ML in academic research and teach it. Some come close to what is done in "real-world", e.g. training and evaluating LLMs with real-world datasets.

Visualizing 3D matrix multiplications, and getting comfortable with it. Then there's basic calculus in understanding gradient descent. Can't think of any other advanced math that was necessary to grok the innermost workings of most models today.

Source: I won a silver medal in a kaggle competition after 6 months of ML self-learning.

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