Year: 2024
Type: conference-paper
Abstract: This paper mainly introduces an application of linear algebra in artificial neural networks to simplify complex operations. All the parameters in a neural network can be stored in matrices, and this method uses matrix multiplication to complete data processing succinctly and efficiently. For example, a higher-level application of artificial neural networks is deep learning, which realizes the deep algorithm of filtering data by multiplying matrices and eigenvectors. Through linear transformation, many complicated problems in neural networks and their applications will be greatly simplified.
Authors: Kaiyu Jin, Qingyang Ma, Haozhe Jia
Institution: Beijing University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410739741