Noninvasive monitoring is an important internet-of-things (IoT) application, which is made possible by the advances in radio-frequency (RF) based detection technologies. Existing techniques however rely on the use of antenna array, and their size greatly limits the applicability of in-home monitoring. Another deficiency is that the technology so far is not applicable to multi-person scenarios. In this paper, we propose our system termed 'DeepMining' which is a single-antenna Doppler radar system that can simultaneously track the breathing rates and heartbeats of multiple persons with high accuracy. DeepMining uses a number of signal observations over a period of time as input and returns the trajectory of the breathing and heartbeat rates of each person. The extraction is based on frequency separation algorithms using successive signal cancellation. The proposed system is implemented using the selfinjection locking (SIL) radar architecture and tested in a series of experiments, showing accuracies of 95% and 90% for two and three subjects, respectively, even for closely located persons.