COMPARATIVE ANALYSIS OF ERRORS IN NEURAL NETWORKS OF ARTIFICIAL INTELLIGENCE
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Neural Networks, Artificial Intelligence, Error Analysis, Training Errors, Generalization, Model Optimization, Error Detection, AI Performance.##article.abstract##
This paper presents a comparative analysis of errors in neural networks used in artificial intelligence (AI) systems. Neural networks are central to many AI applications, but their performance is often influenced by various types of errors that can affect their accuracy and reliability. The study investigates the different categories of errors, including training errors, validation errors, and generalization errors, and examines the impact of these errors on the overall performance of AI models. We compare different approaches to error detection, correction, and minimization, highlighting the challenges and potential solutions for enhancing the robustnes of
neural networks. The findings aim to provide insights into improving error handling and optimization strategies in AI systems, thereby contributing to the development of more reliable and efficient AI models.
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