Deep learning enriches Local Digital Twins (LDTs) by allowing cities to interpret complex data, thus advancing towards adaptive, autonomous systems. While it offers powerful capabilities for predicting and optimizing urban scenarios, the challenge lies in aligning these advanced models with public needs rather than succumbing to technological hype. Balancing potential transformative benefi ts against resource demands and societal trust is critical. The proposed solution involves leveraging neural networks for computer vision tasks, NLP for processing citizen feedback, and reinforcement learning for dynamic urban management. These deep learning techniques enable real-time monitoring, enhance decision-making, and support adaptive city management. However, deploying these technologies necessitates careful governance, ensuring transparency, and adhering to ethical standards. This learning unit explores the application of deep learning in various LDT contexts, such as computer vision for infrastructure monitoring and NLP for participatory governance. Through practical exercises and critical discussions, trainers focus on the ethical implications and governance challenges. Learners are encouraged to use the Triple Loop Learning framework to assess true value, question assumptions, and reframe societal purposes in their implementation.