DEEP LEARNING-BASED DATA PROCESSING AND PATTERN RECOGNITION METHODS

Deep learning technologies have become one of the most important areas of artificial intelligence and data science in recent years. These methods provide high efficiency in processing large-scale data, extracting meaningful information, and identifying complex patterns. This article analyzes deep learning-based data processing techniques and modern pattern recognition methods. The study focuses on convolutional neural networks (CNN), recurrent neural networks (RNN), and multilayer neural architectures used in image recognition, speech processing, medical diagnostics, and intelligent systems. In addition, the article presents mathematical models, performance evaluation methods, and practical implementation examples using Python. Experimental results demonstrate that deep learning models significantly improve accuracy and automation in pattern recognition tasks compared to traditional machine learning approaches.