This deep dive unpacks reverse-mode automatic differentiation—the generalized mathematical foundation behind neural network backpropagation. Through computational graphs and Python implementations, we reveal how this algorithm efficiently calculates gradients for complex functions, enabling modern deep learning frameworks.