02 - Differential Calculus for DL: Gradients and Backpropagation

How neural networks learn: partial derivatives, chain rule (the core), backpropagation step-by-step. NO limits and epsilon-delta—visual intuition of gradients. Computational graphs, automatic differentiation. Manual NumPy implementation, why PyTorch ...