EurekAlert!

Not so simple machines: Cracking the code for materials that can learn

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. The ...
InfoQ

Deep Learning Pioneer Geoffrey Hinton Publishes New Deep Learning Algorithm

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...
Quanta Magazine

Neuron Bursts Can Mimic Famous AI Learning Strategy

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 ...
Diginomica

Generative AI needs better context - you can't talk about Large Language Models without reverse neural networks and backpropagation

The hype over Large Language Models (LLMs) has reached a fever pitch. But how much of the hype is justified? We can't answer that without some straight talk - and some definitions. Time for a ...
Visual Studio Magazine

How To Use Resilient Back Propagation To Train Neural Networks

Resilient back propagation (Rprop), an algorithm that can be used to train a neural network, is similar to the more common (regular) back-propagation. But it has two main advantages over back ...