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 ...

“Deep neural networks (DNNs) are typically trained using the conventional stochastic gradient descent (SGD) algorithm. However, SGD performs poorly when applied to train networks on non-ideal analog ...

Researchers at Google have open-sourced EvoLved sIgn mOmeNtum (Lion), an optimization algorithm for training neural networks, which was discovered using an automated machine learning (AutoML ...

Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.

Liquid neural networks are inspired by biological neurons to implement algorithms that remain adaptable even after training.

Artificial neural networks process data in a manner similar to the human brain.

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 ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at ...

The best way to understand neural networks is to build one for yourself. Let's get started with creating and training a neural network in Java.