Back-propagation is the most common algorithm used to train neural networks. There are many ways that back-propagation can be implemented. This article presents a code implementation, using C#, which ...

For example, if you have a neural network that predicts the scores of two basketball teams in an upcoming game, ... In this case, the back-propagation algorithm in method UpdateWeights succeeds in ...

The simplest form of backpropagation involves computing the gradient — the optimization algorithm that’s used when training a machine learning model — of a loss function with respect to the ...

The backpropagation algorithm, which is based on the derivation of a cost function, is used to optimize the connecting weights, but neural networks have a lot of other knobs to turn.

Obtaining the gradient of what's known as the loss function is an essential step to establish the backpropagation algorithm developed by University of Michigan researchers to train a material.

A new technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...