Backpropagation is the fundamental algorithm that enables neural networks to learn from data. It works by calculating how much each connection weight contributes to the overall error, then adjusting these weights to improve the network's predictions. This process involves propagating error information backward through the network layers.
The forward pass is the first step in backpropagation. Input data flows through the network from left to right. Each neuron performs a weighted sum of its inputs plus a bias term, then applies an activation function. This process continues layer by layer until we reach the output, which represents the network's prediction.
After the forward pass, we calculate the loss by comparing the network's prediction with the actual target value. Then comes the backward pass, where error information flows backward through the network. Using the chain rule of calculus, we compute gradients that tell us how much each weight contributed to the total error.
The final step is parameter update. Using the calculated gradients, we adjust each weight by subtracting the gradient multiplied by a learning rate. This learning rate controls how big steps we take. The process repeats for many epochs, and the loss gradually decreases as the network learns to make better predictions.
To summarize, backpropagation is the foundation of neural network training. It combines forward prediction, error calculation, gradient computation, and parameter updates in a continuous cycle. This elegant algorithm enables networks to automatically learn complex patterns from data, making modern artificial intelligence possible.