A Study of Information-Based Teaching Strategies for the Saxophone Based on Deep Learning

Abstract

Education is one of the core elements in building the career of an individual. It needs proper strategies and techniques to fulfill the modern world’s requirements, such as intelligent learning systems, intelligent management systems, and intelligent computational systems. At present, there is a dearth of systematic debate on how to proceed along the road of machine learning (ML) and education. As a result, this study focuses on the use of artificial intelligence (AI) to promote saxophone informatization teaching strategies, particularly the new strategies brought by deep learning (DL) to saxophone teaching from the perspectives of teaching resources, teaching environment, teaching and learning strategies, teaching management, and teaching evaluation. A matrix decomposition strategy with dynamic weight learning is suggested by keeping the earlier aspects in consideration, which is used to produce a recommendation algorithm that fundamentally incorporates multiple contextual features such as geographic, temporal, and social characteristics, as well as the weight parameter learning process, and essentially constitutes the linear fusion technique’s building approach. All the experiments are carried out on the yelp dataset in order to check if the recommended algorithm is effective or not. The performance of the suggested method is compared to the benchmark algorithms in order to prove that the dynamic weight parameter learning technique is as effective as gradient descent. A comparison of the algorithm that employs one contextual element alone vs the method that uses three contextual factors is also conducted to demonstrate that the linear fusion of several components improves the system’s recommendation performance.