# Neural Nets: Backpropogation from Conjugate Gradient Descent

In neural nets, the back-propagation algorithm is 'based on' gradient descent, optimizing over a cost function $C(w,b)$ of the weights and biases of the network.

Conjugate gradient descent has way faster convergence than standard gradient descent (and stochastic gradient descent). Is there a similar back-propagation algorithm based off of conjugate gradient descent?