The proliferation of the Internet of Things (IoT) and cloud services has given rise to the edge computing paradigm, where data is processed partly or entirely at the edge of the network, rather than solely in the cloud. Edge computing can address problems such as latency, limited battery life of mobile devices, bandwidth costs, security, and privacy [1, 2, 3]. Typical applicable scenarios based on edge computing include video analytics, smart home, smart city, and collaborative edge.With the development of deep learning techniques, research on employing deep learning to develop intelligent edge systems is emerging. In this dissertation, we aim to investigate how deep learning can process data on source-constrained individual edge devices in real time and how deep learning can process data by utilizing collaborative edge devices to provide better services. We build several critical systems, including video analytics, driving anomaly detection, arm posture tracking, and device orientation tracking. In the video analytics system, we combine deep learning with traditional image processing techniques to achieve real-time object detection on mobile devices without offloading. In the driving anomaly detection system, we train deep learning models for driving anomaly detection by leveraging the information from collaborative peer devices to provide better accuracy. In the arm posture tracking system, we employ multitask learning to track the orientation and location of the wrist simultaneously, which significantly improves the latency compared to the conventional methods. In the device orientation tracking system, we develop a deep reinforcement learning framework to train an agent that adjusts the parameters of a conventional orientation tracking method in response to changing environments. As IoT systems continue to grow in complexity and size, preserving training data has become an increasingly important challenge. In our future work, we plan to investigate the use of representation learning to address this issue. By leveraging representation learning techniques, we aim to develop a more robust and efficient method for saving and utilizing training data in IoT systems. This could enable better performance of IoT systems, which in turn could lead to significant improvements in a variety of fields, such as healthcare, transportation, and manufacturing.