# Are calculus and differential geometry required for building neural networks?

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?

• What level are you hoping to build neural networks at? You only need a brief overview of calculus and statistics if you want to use a toolkit like Keras or PyTorch. If you want to understand all the details, and build everything yourself in C++ or similar, then your need more. Commented Aug 6, 2021 at 7:44
• Building it from scratch. Not using any external library. I've already studied statistics and probability. Ok so I need Calculus. Commented Aug 6, 2021 at 8:01
• As a side note, "perceptrons" and "neural networks" may not be the same thing. People usually use the term perceptron to refer to a very simple neural network that has no hidden layer. Maybe you meant the term "multi-layer perceptron" (MLP).
– nbro
Commented Aug 6, 2021 at 10:17
• @nbro FYI: I asked a new question based on your comment.
– R.M.
Commented Aug 7, 2021 at 13:48