| dc.description.abstract | In recent years, artificial intelligence has been rapidly developing, and with the progress of the internet, 5G is also widely used, and the metaverse is entering our lives. The foundation of this cannot be separated from deep learning, and TensorFlow is an important deep learning framework for language understanding tasks of machine learning. TensorFlow simplifies and improves the code library of DistBelief for computer scientists, making it faster and better, so that users can easily use the deep learning algorithms inside the code library to construct models. Nowadays, we widely use deep learning to analyze big data, among which, the Convolutional Neural Network (CNN) has good performance in image recognition. This article will introduce how to use Keras to construct a CNN model to analyze school student performance data. We will establish a model, when we have this student′s semester performance data, we can use this model to predict the student′s performance in the next semester.
After we acquire student performance data, it contains the grades and multiple fields of each student, these fields represent different meanings. Among them, we find out each student′s grades for each subject in each semester to analyze student performance. We use R language to process data, select college students′ data, and convert students′ performance data in each semester into images. We use students′ previous few semester grades to do linear regression to predict the grade for the next semester.
This article uses Keras to create a CNN model in R language, we find that the model′s parameter settings do not have a certain rule, the number of epochs is not the more the better and the learning rate is not the slower the better. We need to continuously train the model and adjust the model parameters to find the best model for processing student performance data.
In the early days, linear regression was commonly used for predicting values. Now that artificial intelligence is becoming more and more proficient, this article will explore whether the MLP and CNN models established using deep learning can have better results than traditional linear regression models, and the calculation of the machine will not pay too much the price.
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