Training a neural network is the process of finding a set of weight and bias values so that for a given set of inputs, the outputs produced by the neural network are very close to some target values.

This repository contains an implementation of a backpropagation engine for neural networks in R, alongside a neural network script. It includes the following main files: FinalBackpropagationEngine.R: ...

Can you give a visual explanation for the back propagation algorithm for neural networks? Let's assume we are really into mountain climbing, and to add a little extra challenge, we cover eyes this ...

Backpropagation and neuroevolution are used in a Lamarckian evolution process to train a neural network visual controller for agents in the Quake II environment. In previous work, we hand-coded a ...

Here we have presented only examples where spiking backpropagation was applied to feed-forward networks, but an attractive next goal would be to extend the described methods to recurrent neural ...

Levenberg-marquardt backpropagation training of multilayer neural networks for state estimation of a safety critical cyber-physical system. IEEE Transactions on Industrial Informatics.

Training a neural network is the process of finding a set of weight and bias values so that for a given set of inputs, the outputs produced by the neural network are very close to some target values.