Modulation identification and target classification are important functions for intelligent RF receivers. These functions have numerous applications in cognitive radar, software-defined radio, and efficient spectrum management. To identify both communications and radar waveforms, it is necessary to classify them by modulation type. For this, you can extract meaningful features which can be input to a classifier. While effective, this procedure can require effort and domain knowledge to yield an accurate identification. A similar challenge exists for target classification. In this workshop, we will demonstrate data synthesis techniques that can be used to train Deep Learning networks for a range of radar communications systems including: • Data pre-processing and wave generation • Develop a model using a pre-trained model (SqueezeNet) using the Deep Network Designer app • Deep Learning modeling