The interest on applications where machine learning algorithms and communications are combined has been on a rising in recent years. Machine learning and neural networks are being advocated as a way of improving the performance of several functions across all layers of future communication systems. Furthermore, in applications where complexity reduction is essential for the system feasibility at the cost of an affordable performance loss, more efficient systems might be achieved with the aid of machine learning algorithms. Signal detection for multiple-input multiple-output (MIMO) systems has become a hot topic in recent years given its prominent role in fourth and fifth generations of mobile networks. However, the computational complexity in MIMO systems can become prohibitive when the number of antennas is high, as in massive MIMO, for example. Therefore, by leveraging neural networks architectures we propose a deep unfolded detector, whereby the probability data association (PDA) detector algorithm is adapted and enhanced by means of neural network learning capabilities. We unveil that the proposed detector is orders-of-magnitude less complex than the PDA detector and specially than the optimum detector, yet presenting no severe penalties in performance in terms of bit error rate (BER).