👉 Sequential Engineering, or Seq2Seq, is a machine learning paradigm focused on processing sequential data, such as text, speech, or time-series, by mapping an input sequence to an output sequence. It typically involves two main components: an encoder and a decoder. The encoder processes the input sequence, converting it into a compact representation that captures its essential features and context. The decoder then uses this representation to generate the output sequence, one element at a time. This approach is widely used in tasks like machine translation, speech recognition, and text generation, where understanding the order and relationships within sequences is crucial. Seq2Seq models can be further enhanced with attention mechanisms, allowing the decoder to focus on relevant parts of the input sequence during output generation, improving accuracy and coherence in the results.
