Deep learning continues to show benefit in significant aspects of sensor systems including computer vision, speech recognition, and cybersecurity. In parallel, radio frequency (RF) systems have become increasingly complex and the number of connected devices will significantly increase as IoT and 5G systems become prevalent. Deep learning within RF systems is a new field of research that shows promise for dealing with a congested spectrum, brining reliability enhancements, and simplifying the ability to build effective signal processing systems. The utilization of deep learning algorithms within RF technology has shown superior results and the ability to classify signals well below the noise floor when compared to traditional signal processing methods. Working with strategic partners, we have designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and deep learning with an NVIDIA Jetson TX2. We discuss RF specific system performance, collection of RF training data, and the software used by the community to create custom applications. Additionally, we will present data demonstrating applications in the field of deep learning enabled RF systems.