Sequence-to-Sequence Models
Sequence-to-sequence (Seq2Seq) models are a class of deep learning architectures designed to transform input sequences into output sequences of potentially different lengths. They are primarily built using recurrent neural networks (RNNs) or transformers, with an encoder-decoder structure where the encoder processes the input and the decoder generates the output. These models are widely used for tasks like machine translation, text summarization, and speech recognition.
Developers should learn Seq2Seq models when working on natural language processing (NLP) applications that involve sequence transformation, such as translating text between languages or generating responses in chatbots. They are essential for handling variable-length inputs and outputs, making them ideal for real-world scenarios where data sequences vary, like in automated customer support or content generation tools.