this post was submitted on 08 Nov 2023
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Machine Learning

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The absolute basic mathematics that is required to understand basic ML/DL are calculus, linear algebra, probability and some convex optimisation. We are all aware of that.

But ML and DL has become a vast field both in breadth and depth. A single person can't understand the field entirely. There are specialistions and sub-specialisations and further more.

If you work in a branch of ML/DL research where some other math fundamentals are needed to understand research papers and do innovative research, can you mention your field of work and the math fundamentals that are required to gain entry into your field?

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[–] Western-Image7125@alien.top 1 points 1 year ago (2 children)

Ok this is… a lot of stuff. Understand probability through measure theory?

[–] ToxicTop2@alien.top 1 points 1 year ago

Sounds freaking fun to me!

[–] esnfdanwm423rsefte@alien.top 1 points 1 year ago

Try not to get to focused on knowing all of such lists but try to skim at least what seems possible because it's nice to have a toolbox in your head.

Most students that still get hired don't know much of proper code architectures, patterns or code decoupling that is very much essential for proper development but still get to learn on the job. Having been a ML engineer for a couple of years I still haven't picked up a lot of statistics or sometimes even architectures because they have never been relevant to our use cases.

At most companies I have been and interviewed at you are expected to learn, not know. You need to be over a base line for the jobs essentially but you should be substantiate why you are a good learner that can pick up anything. One note to this though is if you only limit your search to the biggest companies with unlimited applicant pools. The baseline will definitely rise for minimum requirements and arbitrary filters will be set up just to get rid of the masses and just interviewing the most notable outliers.