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Recurrent Neural Networks (RNNs) are a type of neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or spoken words. RNNs are particularly useful for tasks involving sequential data because they have loops that allow information from previous steps to influence the current step's computations. In the context of Gated Recurrent Units (GRUs), RNNs can be thought of as a more general class of models, with GRUs being a specific type of RNN architecture. GRUs are designed to address some of the challenges that arise in training traditional RNNs, such as the vanishing or exploding gradient problem. By incorporating gating mechanisms, GRUs can selectively update their hidden state over time, making them more effective at processing long sequences of data compared to traditional RNNs. Thus, RNNs under the parent category of Gated Recurrent Units represent a family of models that are capable of learning from sequential data, with GRUs being one particular instantiation of this general concept. The focus is on how RNNs can be used to model sequences and how GRUs improve upon traditional RNN architectures for processing long sequences.