James McCaffrey explains the common neural network training technique known as the back-propagation algorithm.

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 ...

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

Back Propagation is a common method of training artificial neural networks so as to minimize objective function. This paper describes the implementation of back propagation algorithm.

The challenge of speeding up AI systems typically means adding more processing elements and pruning the algorithms, but those approaches aren’t the only path forward. Almost all commercial machine ...

In this work, a gradient method with momentum for BP neural networks is considered. The momentum coefficient is chosen in an adaptive manner to accelerate and stabilize the learning procedure of the ...

Neural networks made from photonic chips can be trained using on-chip backpropagation – the most widely used approach to training neural networks, according to a new study. The findings pave the ...

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 ...