Deep Learning and Its Application to Unstructured Data Analysis

The purpose of the work is to explore the application of deep learning methods for the analysis of unstructured data, including texts, images and audio. Particular attention is paid to the use of convolutional neural networks (Convolutional Neural Network, abbreviated as CNN) for classifying images and transformer models BERT (Bidirectional Encoder Representations from Transformers, Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer transformer) for data processing. Various models were tested on open-access datasets, and the results showed high accuracy of deep learning compared to traditional methods. The study confirms the potential of deep learning to improve the analysis of unstructured data.