u/70X1C17Y

Beginner in ML, I know the basics of models and how they work. And I have a decent foundation in linear algebra and calc (1-3) and taking calc 4/Diff EQ next semester. Currently reading notes from stanford CS229 and Elements of Statistical Learning. I understand the surface level math but there are a lot of partial diff eq on matrices to derive these formulas like maximizing log likelihood that is hard to wrap my head around. Is knowing how to derive these equations genuinely useful or not really as long as you know what it does and how it works (like how, why, and when we use softmax).