There are two different techniques for training a neural network: batch and online. Understanding their similarities and differences is important in order to be able to create accurate prediction ...

Training a neural network is the process of finding a set of weights and bias values so that computed outputs closely match the known outputs for a collection of training data items. Once a set of ...

Welcome to Neural Basics, a collection of guides and explainers to help demystify the world of artificial intelligence. One of the most influential technologies of the past decade is artificial neural ...

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 ...

We’re going to talk about backpropagation. We’re going to talk about how neurons in a neural network learn by getting their math adjusted, called backpropagation ...

Neural networks are all the rage right now with increasing numbers of hackers, students, researchers, and businesses getting involved. The last resurgence was in the 80s and 90s, when there was little ...

This blog post is the second in our Neural Super Sampling (NSS) series. The post explores why we introduced NSS and explains its architecture, training, and inference components. In August 2025, we ...

Deep neural networks can perform wonderful feats thanks to their extremely large and complicated web of parameters. But their complexity is also their curse: The inner workings of neural networks are ...

Researchers have used brain-like "neural networks" to analyze key distortions in space-time 10 million times faster than conventional methods can do so. The new study trained an ...