By far the most common neural network training technique (but not necessarily the best) is to use what's called the back-propagation algorithm. Although there are many good references available that ...

Backpropagation, short for "backward propagation of errors," is an algorithm that lies at the heart of training neural networks. It enables the network to learn from its mistakes and make ...

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 ...

An enormous amount of variety is encompassed within the basic structure of a neural network. Every aspect of these systems is open to refinement within specific problem domains. Backpropagation ...

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 ...

Artificial intelligence (AI) has come a long way since its inception, and backpropagation is one of the most fundamental algorithms that has contributed to the development of machine learning. It is a ...

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 ...

The simple hill-climbing algorithms used in the first neural networks didn't scale for deeper networks. As a result, neural networks fell out of favor in the 1970s and early 1980s—part of that ...