These notes cover the first-principles mathematical foundation of neural networks:
- Multivariate Chain Rule: Tracking gradients through computational nodes.
- Matrix Calculus: Jacobians, Hessians, and dimension preservation during backprop.
- Layer Derivations: Fully connected forward and backward pass equations.