To someone not active in the Tensorflow community, it is really not obvious what this is for. What are typical use cases? Why does the world need this?
Encoder-Decoder models are a very common technique in sequence-to-sequence models for deep learning. These models have had very big wins lately in NLP tasks such as translation, POS tagging, dialogue generation, etc.
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To someone not active in the Tensorflow community, it is really not obvious what this is for. What are typical use cases? Why does the world need this?
Encoder-Decoder models are a very common technique in sequence-to-sequence models for deep learning. These models have had very big wins lately in NLP tasks such as translation, POS tagging, dialogue generation, etc.
The Tensorflow documentation has an okay writeup about seq2seq models: https://www.tensorflow.org/tutorials/seq2seq
The author of the library also has a small blurb about it on his blog: http://www.wildml.com/deep-learning-glossary/#seq2seq