Mar 10, 2021 · The encoder-decoder architecture for recurrent neural networks is the standard neural machine translation method that rivals and in some cases outperforms classical statistical machine...

Mar 11, 2021 · In this article, I aim to explain the encoder-decoder sequence-to-sequence models in detail and help build your intuition behind its working. For this, I have taken a step-by-step...

Aug 7, 2019 · The encoder-decoder recurrent neural network architecture is the core technology inside Google’s translate service. The so-called “Sutskever model” for direct end-to-end machine translation. The so-called “Cho model” that extends the architecture with GRU units and an attention mechanism.

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

Jan 12, 2024 · We can use CNN, RNN & LSTM in encoder-decoder architecture to solve different kinds of problems. Using a combination of different types of networks can help to capture the complex relationships between the input and output sequence of data.

Figure 10.3 Basic RNN-based encoder-decoder architecture. The final hidden state of the encoder RNN serves as the context for the decoder in its role as h 0 in the decoder RNN.

Aug 14, 2019 · The encoder-decoder model for recurrent neural networks is an architecture for sequence-to-sequence prediction problems where the length of input sequences is different to the length of output sequences.

Figure 8.17 Translating a single sentence (inference time) in the basic RNN version of encoder-decoder ap-proach to machine translation. Source and target sentences are concatenated with a separator token in between, and the decoder uses context …

In this tutorial we’ll cover encoder-decoder sequence-to-sequence (seq2seq) RNNs: how they work, the network architecture, their applications, and how to implement encoder-decoder sequence-to-sequence models using Keras (up until data preparation; for training and testing models, stay tuned for Part 2).

Following the design of the encoder–decoder architecture, we can use two RNNs to design a model for sequence-to-sequence learning. In encoder–decoder training, the teacher forcing approach feeds original output sequences (in contrast to predictions) into the decoder.