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The paper proposes an approach for fast small signal stability assessment on a short data window using deep learning algorithms. This paper shows that the proposed deep convolutional neural networks (CNNs)-based assessment approach is faster than traditional methods (i.e. Prony’s method). The evaluated CNNs are fully convolutional network (FCN), CNN with sub-sampling steps performed through max pooling (Time LeNet), time CNN, fully convolutional network with attention mechanism (Encoder), and CNN with a shortcut residual connection (ResNet). The proposed approach is validated on different synthetic measurement data sets generated from the IEEE 9-bus system that is used as a reference, and further applied to a 769-bus system representing a region in the U. S. Eastern Interconnection. We show that precision and recall are more informative metrics than accuracy for the reliability of the stability assessment process using the proposed methodology. In addition, the method’s efficiency is compared to classical Prony method.