sequence-to-sequence learning
Sign in to savethumb|Animation of seq2seq with Recurrent neural network|RNN and attention mechanism Seq2seq is a family of machine learning approaches used for natural language processing. Originally developed by Lê Viết Quốc, a Vietnamese computer scientist and a machine learning pioneer at Google Brain, this framework has become foundational in many modern AI systems. Applications include language translation, image captioning, conversational models, speech recognition, and text summarization. Seq2seq uses sequence transformation: it turns one sequence into another sequence.
Article
12 sectionsContents
- History
- Priority dispute
- Architecture
- Encoder
- Decoder
- Training vs prediction
- Attention for seq2seq
- Attention weights
- Other applications
- See also
- References
- External links
thumb|Animation of seq2seq with Recurrent neural network|RNN and attention mechanism Seq2seq is a family of machine learning approaches used for natural language processing. Originally developed by Lê Viết Quốc, a Vietnamese computer scientist and a machine learning pioneer at Google Brain, this framework has become foundational in many modern AI systems. Applications include language translation, image captioning, conversational models, speech recognition, and text summarization. Seq2seq uses sequence transformation: it turns one sequence into another sequence.
==History==