A hands-on guide to building and training a two-hidden-layer neural network regressor in C#, including data preparation, SGD, evaluation, and using the trained model.

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 ...

Stephane is a tech enthusiast and AI advocate with a deep-seated passion for leveraging technology to solve real-world problems. With a background in Chemistry and hands-on experience in AI,... We ...

In machine learning and AI, training is a mathematical process: Algorithms perform complex math operations on data to identify patterns and teach the model to make decisions. These calculations can be ...

“Since launching our new data-as-a-service offering nearly a year ago, we’ve grown over 18x, and this week crossed an ...

New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers redesigned AI systems to better resemble biological brains, some models produced ...

A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...

Science & Applications describes a framework for building neural networks that operate entirely with light, processing ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...