АНАЛІЗ ОСОБЛИВОСТЕЙ СУЧАСНИХ НЕЙРОМЕРЕЖ

This work investigates existing neural networks based on their architectures and functionalities. Various types of neural network architectures, including feedforward, convolutional, recurrent, and attention-based models, are analyzed and categorized. The performance and suitability of each type of neural network for specific tasks are assessed. Additionally, emerging architectures and hybrid models that combine different neural network types are explored. The findings contribute to understanding the strengths and limitations of different neural network architectures, aiding in the selection and optimization of models for various applications.