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 ...

Graph neural networks (GNNs) are a type of neural network architecture and deep learning method that can help users analyze graphs, enabling them to make predictions based on the data described by a ...

AI success depends on whether enterprise data is ready, reachable, and close enough to the workloads that need it. In this eSpeaks episode, Dell Technologies’ Vrashank Jain explains why fragmented ...