The construction of a teacher training system for artificial intelligence professionals in universities oriented to RNN models

Abstract

Abstract In this paper, a custom recurrent neural network is constructed based on RNN. The internal structure of the GRU recurrent neural is mainly analyzed, and the activation function is used to ensure the smooth flow of information in the backward propagation of the neural network. By analyzing the recurrent neural in the processing sequence as well as correlation data, the time-based backpropagation algorithm is constructed. Take advantage of Highway connection in backward propagation to improve the computational speed of the improved recurrent neural network. The moving average method is used to deal with abnormal data and combined with the SGD algorithm to avoid the problem of using all training samples in one iteration so as to establish the optimal model of teacher training time series. The results show that: 48% of the training target factors account for teacher training mainly focused on professional knowledge and 35% on practical ability.