Back-propagation compares neural network actual outputs (for a given set of inputs, and weights and bias values) with target values, determines the magnitude and direction of the difference between ...

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn ...

Neural networks made from photonic chips can be trained using on-chip backpropagation – the most widely used approach to training neural networks, according to a new study. The findings pave the way ...

Learn how backpropagation works using automatic differentiation in Python. Step-by-step implementation from scratch.

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 ...

This week at the MLSys Conference in Austin, Texas, researchers from Rice University in collaboration with Intel Corporation announced a breakthrough deep learning algorithm called SLIDE (sub-linear ...

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.