<![CDATA[An important development in industrial automation is the combination of deep learning with cyber-physical systems (CPS), which allows systems to make data-driven, intelligent decisions with little assistance from humans. With an emphasis on its capacity to handle massive amounts of data for tasks including object detection, semantic segmentation, predictive maintenance, and autonomous control, this research investigates the revolutionary effects of deep learning on CPS. It looks at how technology has developed from early frameworks that relied on visual cues to complex systems that use cutting-edge neural networks that can function in dynamic, unstructured contexts. The study also emphasizes how important it is to integrate ethical frameworks, organizational preparedness, and human-centered design in order to successfully implement CPS. This study analyzes important trends, obstacles, and best practices that influence the application of deep learning in CPS through an extensive examination of recent literature. The significance of CPS in facilitating the Industry 4.0 and Industry 5.0 paradigms—which prioritize sustainability, human-machine collaboration, and real-time adaptation in industrial processes—is given particular attention.]]>
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