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 ...

But it turned out that Hinton’s backpropagation algorithm could easily be split up into bite-sized chunks. So training neural networks turned out to be a killer app for CUDA.

Backpropagation in CNN is one of the very difficult concept to understand. And I have seen very few people actually producing ...

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.

AI engineers solved the credit assignment problem for machines with a powerful algorithm called backpropagation, popularized in 1986 with the work of Geoffrey Hinton, David Rumelhart, and Ronald ...

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 ...

If the backpropagation algorithm estimates that increasing a given neuron’s activity will improve the output prediction, for example, then that neuron’s weights will increase. The goal is to change ...

The backpropagation algorithm has the following steps: Initialize the network weights. Initially, all connection weights are set randomly to numbers between -0.1 and 0.1: ...

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.