Backpropagation is an algorithm used in supervised artificial neural networks to adjust the weights of the connections between neurons in order to reduce the error in the prediction of a model. The goal of backpropagation is to minimize an error function or cost function that measures the difference between the actual output and the expected output.

Backpropagation works by propagating the error backward through the neural network, starting at the output layer and working backward to the hidden layers. For each layer, the relative contribution of each neuron to the cost function is calculated, and these contributions are used to adjust the connection weights.

Backpropagation is based on the chain rule of the derivative, which allows the rate of change of a composite function to be calculated in terms of the rates of change of its individual components. In the context of backpropagation, the chain rule is used to calculate the contribution of each neuron to the cost function, as a function of its inputs and the weights of the connections linking it to subsequent layers.

Backpropagation is one of the most widely used algorithms in supervised neural network training, and has proven to be effective in a wide variety of applications, such as pattern recognition, image classification and natural language processing.