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, bringing 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 to classify signals well below the noise floor when compared to traditional methods. Working with strategic partners Deepwave Digital has designed a software configurable wide-band RF transceiver system capable of performing real-time signal processing and deep learning with an NVIDIA embedded GPU. In this presentation we discuss RF specific system performance, methods of training deep learning algorithms using RF data, the software used to create and deploy the algorithms, and real-time performance benchmarks.