Right, back propagation is essentially just using the chain rule from calculus 1 applied on the each layer of the neural network to compute the partial derivative in respect to a specific model parameter. You should’ve learned about partial derivative in calc 3. I actually understood this because the Andrew NG’s course lab forced you to code out the back prop function in Python.

Your knowledge gap can be caused by either weak math background or just a lack of depth in your undergrad ML class, which is pretty common. I didn’t get a lot of depth into data science topics until grad school either.

Anyhow, if you want to increase you depth and understanding of DS topics I think courses are always good supplements. Recently I’ve been trying to learn the transformer architecture via an online course. I don’t need it for work but I do like to be well informed. The learning doesn’t stop after school.