Neural Networks and Fuzzy Systems
The encoder-decoder architecture is a neural network framework designed to handle input-output pairs of variable lengths, commonly used in sequence-to-sequence tasks like language translation and text summarization. In this setup, the encoder processes the input data and compresses it into a fixed-size context vector, which the decoder then uses to generate the output sequence step-by-step. This design enables effective learning and representation of complex relationships in sequential data, making it a key player in various natural language processing applications.
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