An enormous amount of variety is encompassed within the basic structure of a neural network. Every aspect of these systems is open to refinement within specific problem domains. Backpropagation ...

Here, Sunil Pai and colleagues describe a hybrid photonic neural network (PNN) chip that can perform fast and efficient on-chip backpropagation 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 technical paper titled “Hardware implementation of backpropagation using progressive gradient descent for in situ training of multilayer neural networks” was published by researchers at ...

The most common technique used to train a neural network is the back-propagation algorithm. There are three main variations of back-propagation: stochastic (also called online), batch and mini-batch.

Backpropagation allows the gradients to be efficiently calculated across all layers of the network, making the training process feasible. However, backpropagation is not without challenges.

This is part 2 of this tutorial, and in this is we will look at Backpropagation for entire Convolutional Neural Network. In part 1, we already saw the backpropagation for convolutional operation.