Backpropagation, short for "backward propagation of errors," is a fundamental algorithm used to train neural networks. During the training process, it calculates the gradients of the network's parameters with respect to a defined loss function.
The process starts by making a forward pass through the network, where input data is fed, and the output is obtained. The calculated output is then compared with the desired output, and the difference is quantified as the loss.
Next, the algorithm works in reverse, starting from the output layer and propagating the error backwards. By utilizing the chain rule of calculus, the error is distributed among the neurons in the preceding layers, attributing each neuron's contribution to the overall error.
The gradients of the network's parameters, such as weights and biases, are then computed based on the accumulated errors. These gradients indicate the direction and magnitude of adjustments required to minimize the loss.
Finally, the network's weights and biases are updated using an optimization algorithm, such as gradient descent, which leverages the computed gradients to iteratively refine the model's predictions.
Backpropagation enables neural networks to learn from data by iteratively adjusting their parameters to minimize the discrepancy between predicted and target outputs, thus improving the network's overall performance.