Effectiveness of Parallel Computer Data and Video English Course Based on Neural Network

Abstract

Abstract With the expansion of the scale of deep learning network and the rapid increase of the number of network training parameters, the network training time is getting longer and longer. In deep learning, convolutional neural network reduces the number of some parameters through weight distribution, but the problems such as large number of parameters and long network training time still exist. This paper implements convolutional neural network in distributed environment, and proposes parallel computer data and time-based scheduling strategy to optimize distributed convolutional neural network. This paper also studies the effectiveness of video English curriculum. It is found that with the development of network technology, information technology is more and more widely used in the education industry, and students can realize distance autonomous learning. At present, the network teaching platform is diversified, with the functions of knowledge point learning, online examination and so on. Aiming at the problems existing in the video English course, the project response theory is introduced into the design and development of the platform system. Taking college users as the research object, this paper constructs a data model of College Students' learning behavior, provides all-round services for college students, and improves the effectiveness of video English teaching. At the same time, based on the project response theory, this paper analyzes the current situation of College Students' autonomous learning, so as to improve the efficiency of College Students' autonomous learning. Based on neural network, this paper makes an in-depth study on parallel computer data and video English course, hoping to bring some help to the improvement of College Students' English learning.