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This paper proposes a deep learning (DL)-based joint channel estimation and signal detection in multi-user orthogonal-frequency division multiplexing-non-orthogonal multiple access (OFDM-NOMA) schemes over Rayleigh fading channels. In the considered model, we assume that channel state information (CSI) is not known at the receivers (users), thus, to obtain CSI responses, we use two type pilot insertions (i.e., block type and comb type). According to the received pilot responses and data signal, the proposed DL-based detector (DLD) can detect symbols at all users without requiring any additional operations (e.g., channel estimation, interference canceler, etc.). Then, we evaluate the error performance of the proposed DLD in Monte Carlo simulations and compare the results with the benchmark (i.e., successive interference canceler-based detector (SICD) with perfect CSI at the receiver). It is revealed that …