Sequence-to-Sequence models (Seq2Seq) are a type of artificial neural networks used for tasks such as natural language processing, machine translation, and conversational systems. Seq2Seq models allow a machine to learn from a set of input sequences and output a set of response sequences.
The Seq2Seq model typically consists of a “encoder” and a “decoder”. The encoder takes an input sequence of symbols and outputs a normalized representation of the entire sequence. This normalized representation is then fed into the decoder, which takes the normalized data and outputs a sequence of output symbols.
The main advantage of Seq2Seq models is that the output sequence and the input sequence do not need to have the same length. This allows a Seq2Seq model to handle complex tasks such as translating text from one language to another or generating a response to an input sentence.
Seq2Seq models have found many applications in machine translation, natural language processing, speech recognition, dialog systems, and many more. Recently, Seq2Seq has also been used in the field of computer vision, allowing a machine to “understand” an image and generate an appropriate response.
Seq2Seq models offer an intuitive way to address many machine learning tasks that cannot be easily solved by traditional supervised or unsupervised learning approaches. Seq2Seq models also have the advantage of being able to be trained on relatively small datasets.
