Applications of Linear Algebra in Artificial Neural Networks

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.