Master the math behind backpropagation with a clear, step-by-step derivation that demystifies neural network training.

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 ...

A new model of learning centers on bursts of neural activity that act as teaching signals — approximating backpropagation, the algorithm behind learning in AI. Every time a human or machine learns how ...

The connection weights between these layers are trained by the backpropagation algorithm while minimizing a specific cost function. This framework happens to provide state-of-the-art results ...

Hinton’s jokes belied a serious pursuit: using AI to understand the brain. Today, deep nets rule AI in part because of an algorithm called backpropagation, or backprop. The algorithm enables deep nets ...

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 ...

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.

Dr Hinton popularised a clever mathematical algorithm known as backpropagation to solve this problem in artificial neural networks. But it was long thought to be too unwieldy to have evolved in ...