Deep learning for edge computing has emerged as a pivotal research area, aiming to bring the power of advanced AI models closer to data sources to enable low-latency, intelligent, and real-time services in resource-constrained environments. Research papers in this domain explore techniques for deploying deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models directly on edge and IoT devices, addressing challenges such as limited computation, memory, and energy resources. Studies focus on model compression, pruning, quantization, knowledge distillation, and hardware-aware optimization to make deep learning feasible on edge nodes. Recent works also investigate distributed and collaborative deep learning approaches, including federated learning, edge-cloud co-inference, and model partitioning to enhance scalability and preserve privacy. Applications of deep learning at the edge span autonomous vehicles, smart healthcare, industrial IoT, augmented/virtual reality, and real-time video analytics. Additionally, security- and privacy-aware deep learning frameworks are being explored to protect sensitive data while maintaining model accuracy and performance. Overall, deep learning research for edge computing emphasizes intelligent, adaptive, and efficient AI solutions that bring computation closer to data sources, enabling next-generation real-time and context-aware applications.