Abstract
In recent decades, big data analysis has become one of the most important tasks in the field of information technology. Deep learning, as part of artificial intelligence, has provided effective tools for processing and extracting knowledge from large volumes of data. This article examines key deep learning methods such as artificial neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and autoencoders, which are actively used for analyzing various types of data. It describes approaches to their application, including data preprocessing, distributed computing, and supervised and unsupervised learning methods. The article also discusses current issues such as model interpretability and the development of multimodal data. Furthermore, the prospects of combining deep learning with quantum computing and methods for working with limited amounts of data are analyzed. Despite the successes, deep learning faces several challenges, such as the need for enormous computational resources and ethical issues related to data usage. In conclusion, it is noted that deep learning holds significant potential for solving complex big data analysis tasks, and its development will continue to open new opportunities for various fields of science and industry.