Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...

The goal of the backpropagation algorithm is to adjust input weights so that the network will produce a higher value if it is shown this picture again—and, hopefully, other images containing hot ...

Back-propagation is by far the most common neural-network training algorithm, but by no means is it the only algorithm. Important alternatives include real-valued genetic algorithm training and ...

Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks.

Backpropagation, short for "backward propagation of errors," is an algorithm that lies at the heart of training neural networks.

It is a mathematical method for training neural networks to recognize patterns in data. The history and development of the backpropagation algorithm, including the contributions of Paul Werbos, take ...

However, executing the widely used backpropagation training algorithm in multilayer neural networks requires information—and therefore storage—of the partial derivatives of the weight values ...

Back-propagation is by far the most common neural-network training algorithm, but by no means is it the only algorithm. Important alternatives include real-valued genetic algorithm training and ...