# Why is backpropagation called backwards propagation of error, when it back propagates error derivatives?

Wikipedia says:

The backward propagation of errors or backpropagation, is a common method of training artificial neural networks and used in conjunction with an optimization method such as gradient descent.

Ultimately, backward propagation is used to get the partial derivates of the weights and biases of the network, so that gradient descent can be used.

This means you end up with $\frac{\partial TotalError}{\partial weight_i}$ and $\frac{\partial TotalError}{\partial bias_i}$ at the end, for all weights and biases, but you don't actualy end up with any sort of specific error value for the neurons.

With that in mind, why is it called back propagation of errors, instead of backpropagation of error gradient or something similar?

• It's just a name. I wouldn't look too much into it. The colloquial name is just backpropagation anyway. – Yuval Filmus Mar 1 '17 at 0:56