The Encoder-Decoder RNN is a powerful architecture for problems where both the input and the output are sequences, such as machine translation, text summarization, and question answering. The notebook ...

Sentence Ordering with RNN Encoder-Decoder This project implements a sequence-to-sequence model using an RNN encoder-decoder architecture to solve the sentence ordering task. The model learns to ...

Here, an encoder-decoder based sequence-to-sequence language model has been used for performing text generation and the empirical results show that the model effectively suggests the incomplete ...

The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine ...

The time-series data is a type of sequential data and encoder-decoder models are very good with the sequential data and the reason behind this capability is the LSTM or RNN layer in the network.

Transformers consist of six similar encoders and six similar decoders. Each encoder has two layers – a self-attention layer and a feed-forward Neural Network. The decoder has both layers, but between ...

Text Auto-complete feature suggests a stream of words which complete a user's text as the user types each character. Such a feature is used in search engines, email programs, source code editors, ...