Modeled on the human brain, neural networks are one of the most common styles of machine learning. Get started with the basic design and concepts of artificial neural networks.

How neural networks work—and why they’ve become a big business Neural networks have grown from an academic curiosity to a massive industry.

Can a neural network be constructed entirely from DNA and yet learn in the same way as its silicon-based brethren? Recent ...

A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher. Artificial neurons—the fundamental building blocks of deep neural networks—have survived almost ...

Understanding data encoding and normalization is an absolutely essential skill when working with neural networks. James McCaffrey walks you through what you need to know to get started.

Artificial Neural Networks consist of a number of units which are mini calculation devices. They take in real-valued input from multiple other nodes and they produce a single real valued output.

They can use neural networks to find patterns and associations beyond the subject of chemotherapy. Artificial neural networks, as they currently stand, don't create new answers out of existing data.

Born in the 1950s, the concept of an artificial neural network has progressed considerably. Today, known as “deep learning”, its uses have expanded to many areas, including finance.