I've been studying geometry and linear algebra for months with the goal to build neural networks. But now I'm reading that perceptrons require fitting curves, and curves are not expressed as linear functions. So, I might need to study differential geometry and calculus for building good fitting curves in perceptrons.
I already know how to code and was hoping to get my hands dirty by coding a few neural networks. But should I study calculus and differential geometry before coding?
From this video, I understand that the least squares approximation can be used to fit a curve through a set of points, so maybe linear algebra is enough for building good neural networks?