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 ...

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 ...

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

Five DECADES of research into artificial neural networks have earned Geoffrey Hinton the moniker of the Godfather of artificial intelligence (AI). Work by his group at the University of Toronto laid ...

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 ...

For about a decade, computer engineer Kerem Çamsari employed a novel approach known as probabilistic computing. Based on probabilistic bits (p-bits), it’s used to solve an array of complex ...