Artificial neural networks in the modern world: features of application

DOI: 10.31673/2412-9070.2021.051115

Abstract

This article discusses the peculiarities of the use of artificial neural networks in the modern world. Artificial neural networks are widely used in areas where traditional computers do not work very well. For example, to solve problems where instead of programmed results it is necessary that the system studies, adapts and changes the results depending on the data it receives. Neural networks are also widely used when talking about working with noisy or incomplete data. Due to the ability to learn and adapt, similar to the brain, neural networks form a complete basis and are used in artificial intelligence and, consequently, in machine learning algorithms. As we collect more and more data every year, it makes sense to use deep learning models. In addition, the field of deep learning is also developing rapidly. Many researchers are working hard to develop excellent models of deep learning for specific problems. The use of deep neural networks and other machine learning technologies to solve long-standing problems is evolving rapidly and promises to shape the future of technology. Modern technologies use both controlled and uncontrolled methods. They are likely to become central to practice, and will soon become as widespread and inconspicuous as other technologies we have integrated into everyday use. The scale of the problems that machine learning can help solve is enormous and is likely to evolve rapidly. Integrating machine learning into the daily workflow can expand our capabilities and make modern problem-solving methods more efficient, more focused on higher-order tasks. Neural networks solve problems that require pattern recognition. They are good for pattern recognition, classification and optimization. These include handwriting recognition, face recognition, speech recognition, text translation, credit card fraud detection, medical diagnostics and huge data solutions. You can use it to find connections between templates, convert one type of data to another, and create associations or generalizations between different entities. The article will consider the application of artificial neural networks in various industries.

Keywords: neural network; computer vision.

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