  Polar codes can theoretically achieve very competitive Frame Error Rates. In
practice, their performance may depend on the chosen decoding procedure, as
well as other parameters of the communication system they are deployed upon. As
a consequence, designing efficient polar codes for a specific context can
quickly become challenging. In this paper, we introduce a methodology that
consists in training deep neural networks to predict the frame error rate of
polar codes based on their frozen bit construction sequence. We introduce an
algorithm based on Projected Gradient Descent that leverages the gradient of
the neural network function to generate promising frozen bit sequences. We
showcase on generated datasets the ability of the proposed methodology to
produce codes more efficient than those used to train the neural networks, even
when the latter are selected among the most efficient ones.
