This paper addresses the problem of generating quadcopter minimum snap trajectories for real time applications. Previous efforts addressed this problem by either employing a gradient descent method, or by greatly sacrificing optimality for faster solutions that are amenable for onboard implementation. In this work, outputs of the gradient descent method are used offline to train a supervised neural network. We show that the use of neural networks results typically in two orders of magnitude reduction in computational time. Our proposed approach can be used for warm-starting onboard implementable iterative methods with an “educated ” initial guess. This work is motivated by the application for human-machine interface in which a human provides desired trajectory through a smart-tablet interface, which has to be translated into a dynamically feasible trajectory for a quadcopter. The proposed solution is tested in thousands of different examples, demonstrating its effectiveness as a booster for minimum snap trajectory generation for quadcopters.