In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real sequences. In this paper, neural networks using complex numbers to represent the complex baseband RF signals are considered. A complex backpropagation approach that calculates gradients and a Jacobian, allows for fast optimization of the neural networks. Through simulations, it is shown that complex neural networks require less training samples than their real counterparts and may generalize better in dynamic environments.