Ispol'zovanie neirosetei v prepodavanii vysshei matematiki v tekhnicheskom vuze

Abstract

Due to the rapid development of information technology and artificial intelligence, the use of neural networks is becoming more widespread and significant in various fields of research and training. In higher mathematics, neural networks can be used to improve the learning process of students. For example, they can help create online courses tailored to the individual needs of students. Neural networks can analyze the results of students' work and offer individual recommendations for studying specific topics or problem areas. In addition, neural networks can be used to develop intelligent systems for automatic task verification and pattern recognition. This can help teachers in the process of evaluating students' work and providing feedback. Neural networks can also diagnose problems in understanding the material and offer additional materials or exercises to consolidate skills. Neural networks can also be used to study complex mathematical problems and develop new approaches and methods in higher mathematics. Their ability to process large amounts of data and find complex patterns can help in the study of mathematical structure and the creation of new mathematical models. It is obvious that the use of neural networks in teaching higher mathematics opens up opportunities for more effective and individualized student learning, as well as for the development of mathematics itself. Therefore, this topic is relevant and interesting for further research and applications.