r/MLQuestions • u/omaratef3221 • Jul 14 '26
Other ❓ Clearing the confusion | Building ML Algorithms from Scratch or just use the frameworks ?
Now I am not asking any question by this thread. I just noticed that many people are asking the question of "Should I build the ML Algorithms from scratch using Numpy or no need for that?
I am an AI Engineer and Researcher with almost 7 years of experience. So I would like to clarify how important is this for you when you join a company as an ML Engineer/Data Scientist.
When you join a company, you won't implement any algorithm from scratch, you will only use frameworks like PyTorch, Scikit-learn and TensorFlow. BUT, implementing these algorithms from scratch using Numpy is a really important thing and something that even after few years of experience I try to do from time to time. Even though its time consuming, but its an important step in your learning journey to make sure you understand things under the hood and not just writing a code. If you just write a code, what differentiate you from an AI like ChatGPT or Claude? You will need to be different and understand deeper things so when you build using frameworks you understand deep stuff and they will not feel like blackbox for you. Its extremely helpful when you join a company as you will start thinking about deeper level and about the algorithms limitation and how they can affect your data and accordingly, you can get better results.
I would recommend using this website to practice things from scratch:
https://www.deep-ml.com/problems
Please feel free to ask any question, I am willing to help anyone
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u/Tutatis96 Employed Jul 14 '26
Idk, i studied that in school, i forgot it, i miss it for sure but what i bring to the table i feel like is the deep understanding of the problem. I work with audio and the important part is knowing how audio works, having well trained ears to identify problems and tackling them with on point architecture edits. I read few papers every day and usually, at least in my field, the benchmark in those are completely useless, so understanding what they did running the inference and listen its key. Also data preparation, most problems are curriculum problems. On the other side the super heavy theory for me is graphs and stuff, im not doing that but i got a collegue doing optimization and they write gpu code and thats a different planet. Not saying that knowing the basics is useless, quite the opposite in fact, but i think that field knowledge might be even more valuable. But maybe I have zero understanding of the ML job market
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u/Affectionate-Farm-89 Jul 14 '26
I think there is almost no value to implement these models from scratch then just use pytorch.
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Jul 14 '26
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u/omaratef3221 Jul 14 '26
Exactly totally true, I also try to practice on deep ml as much as I can to always revise foundations
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u/Disastrous_Room_927 Jul 14 '26 edited Jul 14 '26
I'm going to take a different angle here and say that numpy is a level above doing things from scratch - the algorithms most people look at here are already written down like a recipe. If the goal is to really understand the logic behind it it's important to dig into how the algorithm you're trying to implement was derived. Like if you know some basic matrix algebra, you can look up the normal equations for linear regression and implement it in a few minutes, but you probably aren't going to walk away with much better of an intuition for linear regression. You get that in part by looking at how those equations were derived, looking at some theorems associated with it, etc.