In the early 1980s Hopfield, now a professor emeritus at Princeton University, and his colleagues devised and refined an artificial neural network—the so-called Hopfield network—inspired by ...

Because physics has contributed tools for the development of machine learning, it is interesting to see how physics, as a research field, is also benefitting from artificial neural networks ...

Deep neural networks are at the heart of artificial intelligence, ranging from pattern recognition to large language and ...

NTT Research, Inc. and NTT R&D, divisions of NTT (TYO:9432), researchers presented twelve papers at the forty-second International Conference on Machine Learning (ICML), held July 13-19, 2025 in ...

Neural networks are a subset of machine learning, which is a technique used to help computers learn using training that is modeled on results gleaned from large data sets.

Despite the AI hype, tools are proving valuable for leading-edge chip manufacturing. More aggressive feature scaling and ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

In this video, we will understand what is Keras and Tensorflow. Tensorflow is a free and open-source library for machine ...

Artificial neural networks that use machine learning have been used in several programs at Mayo Clinic. One of these ANNs helps physicians doing colonoscopies improve their accuracy. There's something ...

An artificial neural network (ANN) is a type of machine learning model inspired by neurons in the brain. 1,2 The individual components of the ANN receive information in numerical form, process it, and ...

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.