What does backpropagation require for its operation?

Backpropagation is an essential algorithm used in training artificial neural networks that adjusts the weights of the network based on the error of its predictions. This process requires a combination of input vectors and target vectors for its operation.

Input vectors represent the data fed into the network, while target vectors are the expected outputs that the network should ideally produce. During the training process, the network makes predictions based on the input vectors, and these predictions are compared to the target vectors to assess the performance of the network.

When backpropagation is performed, the algorithm calculates the gradients of the loss function (which measures the error between predicted and target values) with respect to the network’s weights. This is done by moving backward through the network, layer by layer, using the differentiated loss function to update the weights. The presence of both input and target vectors is crucial: the input vectors enable the network to make predictions, and the target vectors provide the necessary reference to compute the error and update the model’s parameters accordingly.

This dual requirement distinguishes backpropagation from other processes that may only rely on either inputs or targets separately, which would not suffice for learning in a supervised learning context.