Optimization and Adaptation of Neural Networks Based on Existing Architectures

This paper presents a comprehensive study of methods for optimizing and adapting neural networks based on existing architectures, with a focus on enhancing their efficiency, flexibility, and energy performance under limited computational resources. The relevance of the topic is driven by the growing demand for powerful AI models that can operate in real time and adapt to new tasks with minimal resources. Special emphasis is placed on methods for reducing training time, minimizing computational costs, and improving accuracy through the reuse of existing models.Particular emphasis is placed on techniques to reduce learning time, reduce computational costs, and improve accuracy by reusing existing models. The study also covers the key challenges associated with the integration of optimized neural networks into application systems, and forms recommendations for improving their performance and adaptability. The methodology includes data preparation, normalization, application of the Adam optimizer to effectively minimize the loss function, and experimental testing of models in various scenarios. The practical significance of the obtained results lies in the possibility of their application in high-tech industries: autonomous transport, medical diagnostics, natural language processing, pattern recognition systems, etc., where it is important to ensure the rapid adaptation of models to a changing environment while maintaining high accuracy and reliability.