Backpropagation is an algorithm used for training artificial neural networks, especially feedforward networks with a supervised learning approach. The backpropagation algorithm is a supervised learning method that uses gradient descent to update the weights of the network in order to minimize the difference between the network's predicted outputs and the actual outputs.

The backpropagation algorithm consists of the following steps :

Feedforward : The input data is fed into the network, and the activations are computed for each layer of the network, resulting in the final output.

Calculation of error : The difference between the actual output and the predicted output is calculated and used as the error signal.

Backpropagation of error : The error signal is backpropagated through the network, and the gradients of the error with respect to the weights are computed. Weight update : The weights are updated using the gradients, with the aim of minimizing the error. This is typically done using gradient descent, where the weights are updated in the direction of the negative gradient of the error with respect to the weights.

Repeat : The process is repeated until the error between the predicted and actual outputs is minimized, or a predefined stopping criteria is met.

The backpropagation algorithm is an efficient and effective method for training artificial neural networks, and has been the foundation of many state-of-the-art deep learning models. The algorithm allows the network to learn the relationships between the inputs and outputs through iteratively adjusting the weights, resulting in a model that can make accurate predictions on unseen data.