Abstract: Unsupervised learning provides efficient analytical tools for data-centric Internet of Things (IoT) applications. nonnegative matrix factorization (NMF) is a fundamental tool in unsupervised ...

NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code. NVIDIA has published a ...

Abstract: Matrix factorization is a fundamental characterization model in machine learning and is usually solved using mathematical decomposition reconstruction loss. However, matrix factorization is ...

ABSTRACT: The offline course “Home Plant Health Care,” which is available to the senior population, serves as the study object for this paper. Learn how to use artificial intelligence technologies to ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

College of Computer Science and Technology, Qingdao Institute of Software, China University of Petroleum, Qingdao 266580, China ...

Implemented the Alternating Least Squares (ALS) algorithm to factorize the interaction matrix into user and item latent factors, considering both interaction strength and confidence.