Seq2Seq is a machine learning model architecture used for tasks that involve processing sequential data. It involves training two neural networks, one to generate the input sequence and another to generate the output sequence, with the goal of translating or transforming the input sequence into the desired output sequence.

• Machine translation
• Speech recognition
• Sentiment analysis
• Chatbot development
• Video captioning
• Text summarization
• Name entity recognition
• Video frame prediction

For example, a Seq2Seq model could be trained to translate English sentences into Spanish. The input sequence would be the English sentence, and the output sequence would be the translated Spanish sentence.

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