Backpropagation is a training algorithm used for a multilayer neural NETWORKS. It moves the error information from the end of the NETWORK to all the weights INSIDE the network and thus allows for efficient computation of the gradient.

The backpropagation algorithm can be divided into several steps:

1. Forward propagation of training data through the network in order to generate output.
2. Use target value and output value to compute error derivative with respect to output activations.
3. Backpropagate to compute the derivative of the error with respect to output activations in the PREVIOUS LAYER and continue for all hidden layers.
4. Use the previously calculated derivatives for output and all hidden layers to calculate the error derivative with respect to weights.
5. Update the weights.

Backpropagation is a training algorithm used for a multilayer neural networks. It moves the error information from the end of the network to all the weights inside the network and thus allows for efficient computation of the gradient.

The backpropagation algorithm can be divided into several steps: