Deep Learning-Based Multidimensional Data Analysis Method for Athletes' Education Level

Coaches, athletic trainers, sports dietitians, nutritionists, sports scientists, and medical practitioners are among those who may provide education to athletes. Therefore, they are considering deep learning-based multidimensional data analysis methods efficient source for measuring athletic education levels. With the fierce field, sport is a component of the progression of any country. For this purpose, analyzing and getting the maximum potential for the player's accomplishments, sports analysis has developed into a key devotion. Therefore, to better understand the scope of deep learning-based athlete's education level multidimensional data analysis, the relationship between deep-learning preparation, complex deep learning architect, and labeling patterns with intelligent theory and education level efficiency in athletes. The data was collected from 60 IT (Information and Technology) specialists. Furthermore, the data was collected from various software houses. The collected data was analyzed on Smart PLS 3. The results indicated that our results were significant. Perhaps, using deep learning preparations, complex deep learning architect showed a significant association with the application of intelligent theory and improving the efficiency of education levels of sports athletes.