This collection welcomes submissions on explainability techniques for deep learning neural networks, encompassing diverse neural architectures and ensuring broad applicability to different domains.

Neural networks are now applied across the spectrum of AI applications while deep learning is reserved for more specialized or advanced AI use cases.

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 ...

Tensors are the fundamental building blocks in deep learning and neural networks. But what exactly are tensors, and why are ...

Artificial Neural Networks: Learning by Doing Designed to mimic the brain itself, artificial neural networks use mathematical equations to identify and predict patterns in datasets and images.

Curious about deep learning AI? Here’s your guide on deep learning, how it works and how it is deeply associated with the artificial intelligence world.

Deep learning finally allows machines to tackle problems of similar complexity to those humans can solve, and has been responsible for impressive AI achievements in recent years.

The deep learning pioneers believe that better neural network architectures will eventually lead to all aspects of human and animal intelligence, including symbol manipulation, reasoning, causal ...

Scientists developed a neural network deep learning technique to extract hidden turbulent motion information from observations of the Sun. Tests on three different sets of simulation data showed ...