Forward Pass: Input flows through network, calculating outputs layer by layer
Loss Calculation: Compare prediction with actual output, compute error
Backward Pass: Calculate gradients using chain rule, propagate error backward
Weight Update: Adjust weights using gradient descent: w = w - α∇L
Applications: Training all neural networks • The foundation of deep learning