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Abstract
Machine Learning (ML) and Deep Learning (DL) have revolutionized the field of Artificial Intelligence (AI) by enabling systems to learn from data and make intelligent decisions. This paper presents an in-depth analysis of various ML and DL models, comparing their architectures, applications, strengths, and limitations. By exploring the theoretical foundations and practical applications, we aim to provide a comprehensive understanding of these paradigms, particularly in domains such as computer vision, natural language processing, healthcare, and autonomous systems.
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