If we take a high-level view, a seq2seq model has encoder, decoder and intermediate step as its main components:
If we take a high-level view, a seq2seq model has encoder, decoder and intermediate step as its main components:
The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems, such as machine translation.
Encoder-decoder models can be developed in the Keras Python deep learning library and an example of a neural machine translation system developed with this model has been described on the Keras blog, with sample code distributed with the Keras project.
The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems, such as machine translation.
Encoder-decoder models can be developed in the Keras Python deep learning library and an example of a neural machine translation system developed with this model has been described on the Keras blog, with sample code distributed with the Keras project.
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