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 ...

A neural network is a computational machine-learning model that follows the structure of the human brain. It consists of networks of interconnected nodes or neurons to process and learn from data ...

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

Deep learning systems—a type of unsupervised machine learning—are increasingly used with neural networks. They’re called “deep learning” because they contain large numbers of neural layers.

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Liquid neural networks can spur new innovations in AI and are particularly exciting in areas where traditional deep learning models struggle.

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly a… ...

The integration of deep learning in biodiesel research accelerates feedstock evaluation and optimizes production, making it ...

Neural networks are usually trained with supervised learning. Deep learning uses neural networks that have a large number of “hidden” layers to identify features.

The core of this research lies in the combination of Graph Neural Networks (GNN) and Reinforcement Learning to achieve coordinated control of up to eight robotic arms, enabling efficient and collision ...