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do machine learning experts use more maths or more libraries?

I don't consider myself a machine learning expert yet, but I have had quite a bit of experience working with machine learning experts and veterans in my yet short career. Some of them have done PhD in mathematics while some others have worked extensively as senior software engineers. And therefore, I think it is easy for me to answer whether machine learning experts use more math or more libraries.

The answer which you didn't expect!

They use more libraries and read more maths. If you call a person machine learning experts if he/she can do a lot of machine learning work efficiently, they certainly expertise in both options. Generally, machine learning experts are handed over more open-ended problems, which require both theoretical solution of them as well as building thorough pipelines for cleaning, feature processing, modeling, tuning and interpretable results and visualization creation. And a person who can do so, surely needs a lot of experience in maths, libraries and frameworks.

In terms of maths, every machine learning veteran knows their fair share of statistics, numerical approximations, algorithms and even more core mathematics like analysis, functional analysis, topologies and others. If you meet an experienced person of such type,you will notice that they often read up on good mathematics books focusing on numerical expertise or statistics. These are the habits experts grow to stay on track with the mathematics part of what is growing in the software.

These same people also use lot of libraries in their work so that they do not end up unnecessary codes. It is customary in software to write small, efficient code which are optimized, fast and easy to read. So experts tend to use as much as standard libraries as possible. But they often write custom code because more than often, standard libraries don't fit best in custom machine learning projects.

Conclusion:

So in conclusion, machine learning experts use more libraries in their code, while they read more maths than your average machine learning engineers, to stay ahead of the curve, create and contribute to things. So, if you aspire to become a machine learning expert, then you need to up your game in both maths and software. Stay tuned and subscribe to my blog for technical contents.

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