Оптимізація й адаптація нейромереж на основі наявних архітектур: методи, виклики та перспективи

The article presents a comprehensive study of the development of neural networks based on existing architectures aimed at creating powerful models capable of solving complex machine learning problems in order to increase their effectiveness in solving various artificial intelligence tasks. The relevance of the study is due to the growing requirements for model performance due to hardware resource constraints, the need to respond quickly to new challenges and adapt to specific conditions of use in real time. The study analyzes modern approaches to modifying pre-trained models to improve their performance, reduce computational costs, and adapt to new tasks. The study includes an analysis of existing approaches, identification of the main challenges in integrating optimized neural networks into various applications, and development of recommendations for improving their performance and adaptability. The results of the study allow not only to classify existing methods but also to outline promising areas of technology development that will contribute to the creation of more efficient and flexible artificial intelligence systems. Using a variety of methods and approaches, including transfer learning, fine-tuning, and ensemble methods, this paper discusses the optimization of the process of creating new neural networks by using pre-trained models as a basis. Special attention is paid to methods of transfer learning, model compression, quantization, and neural network architecture search (NAS). The central element of the approach is the deployment of neural networks built on the basis of existing models, which can significantly reduce training time and improve the accuracy of new networks. The research methodology includes collecting and preparing the data set, normalizing the data to ensure stability and efficiency of training, and using the Adam optimizer to minimize the loss function quickly and efficiently. The practical value of the results obtained is manifested in the possibility of their application to develop more energy-efficient solutions in such areas as autonomous vehicles, natural language processing systems, medical diagnostics, and other areas where rapid adaptation to changing operating conditions is critical.