Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

Artificial neural networks are typically trained using data that closely resembles the task they're intended to perform.

In recent years, graph neural networks [1] have been applied to various types of application tasks. This example shows how to train a graph neural network using data calculated with partial ...

Training Transformers Large language models are built around mathematical structures called artificial neural networks. The many “neurons” inside these networks perform simple mathematical operations ...

The researchers found that a formula used in statistical analysis provides a streamlined mathematical description of how neural networks, such as GPT-2, a precursor to ChatGPT, learn relevant patterns ...

An analysis was made of physics-informed neural networks used to solve partial differential equations. The prospects for the implementation of physics-informed neural networks in the MATLAB system are ...

As a result, researchers are increasingly turning to synthetic data to supplement or even replace natural data for training neural networks. “Machine learning has long been struggling with the data ...

In order to improve the load-forecast precision and availability of power system, a method based on Elman neural network and MATLAB is presented to create a load forecast model, which according to the ...