Understand the Maths behind Backpropagation in Neural Networks. In this video, we will derive the equations for the Back ...

The Forward-Forward algorithm (FF) is comparable in speed to backpropagation but has the advantage that it can be used when the precise details of the forward computation are unknown.

A new technical paper titled “The backpropagation algorithm implemented on spiking neuromorphic hardware” was published by University of Zurich, ETH Zurich, Los Alamos National Laboratory, Royal ...

A new model of learning centers on blasts of neural activity that act as teaching signals—approximating an algorithm called backpropagation.

This article describes the backpropagation algorithm, a basic neural network, and its implementation on a Lego Roverbot with Java.

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 ...

Backpropagation has since become one of the most widely used algorithms in the field of artificial intelligence. After the publication of the backpropagation algorithm, it quickly became a popular ...

Backpropagation is not limited to function derivatives. Any algorithm that effectively takes the loss function and applies gradual, positive changes back through the network is valid.

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 ...

NTT Research & Cornell announced that scientists have introduced an algorithm applying deep neural network training to controllable physical systems.

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI.