The sensitivity of optical measurement is ultimately constrained by the shot noise to the standard quantum limit. It has become a common concept that beating this limit requires quantum resources. A deep-learning neural network free of quantum principle has the capability of removing classical noise from images, but it is unclear in reducing quantum noise. In a coincidence-imaging experiment, we show that quantum-resource-free deep learning can be exploited to surpass the standard quantum limit via the photon-number-dependent nonlinear feedback during training. Using an effective classical light with photon flux of about 9×104 photons per second, our deep-learning-based scheme achieves a 14 dB improvement in signal-to-noise ratio with respect to the standard quantum limit.