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by lxe
846 days ago
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I'm very positive I can actually understand the terminology used in discussing machine learning models if it was presented in a way that describes the first principles a little bit better, instead of diving directly into high level abstract equations and symbols. I'd like a way to learn this stuff as a computer engineer, in the same spirit as "big scary math symbols are just for-loops" |
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I'm the same as you: I have no problem grasping complex concepts, I just always struggled with the mathematical notation. I did pass linear algebra in university, but was glad I could go back to programming after that. Even then, I mostly passed linear algebra because I wrote functions that solve linear algebra equations until I fully grasped the concept.
I've found that GPT-4 is very good at taking a math-notation-rich document and just describing it in terms a math-notation-averse engineer would understand.
I was a data engineer for about 6-7 years at various companies, always working together with data scientists who insist that `x_` or `_phi` are proper variable names. Man am I glad to be working with engineers now.