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Recurrent Neural Networks (RNNs) are designed for sequential data processing, with variants like Vanilla RNNs, BiRNNs, LSTMs, and GRUs each having specific drawbacks such as vanishing gradients and limited parallelization. The limitations of these RNN variants have led to the development of the Transformer architecture, which offers parallelization, an attention mechanism for capturing long-term dependencies, and better scalability. Transformers have achieved state-of-the-art performance in various tasks, a

Author

saiimtanweer4

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EN