Research on the reform of university education pedagogy based on deep learning model in the context of information era

Abstract

In this paper, the minimization loss function optimization deep learning algorithm is used to achieve educational pedagogical reform in the context of the information technology era by continuously optimizing the loss function to reach local minima. A feature weight threshold is used to calculate the weights of each output feature, and a weighted average pooling is performed for each one-dimensional vector to produce multiple lengths of nearest neighbor samples. The deep learning model increased the scoring frequency of key values in the reform of higher education pedagogy by about 0.18, with an accuracy rate of 0.91 and an increase of lecture board words by more than 95%. To promote the smooth promotion of college education curriculum reform, the deep learning model recommends promoting the reform of college education pedagogy.

Keywords

• deep learning
• information age
• educational pedagogy reform
• feature weight threshold
• weighted average pooling
• 97d60