A decoder is a crucial component in various machine learning models, particularly in sequence-to-sequence (seq2seq) architectures. It is responsible for transforming encoded information, typically represented as a fixed-length vector, back into a sequence of outputs. This process is essential in applications such as language translation, where the decoder generates words in the target language based on the encoded input from the source language. Decoders can be implemented using different neural network architectures, including recurrent neural networks (RNNs) and transformers, enabling them to handle varying lengths of input and output sequences effectively.