Oct 13, 2025 · In deep learning the encoder-decoder model is a type of neural network that is mainly used for tasks where both the input and output are sequences.

Encoder-decoder models are used to handle sequential data, specifically mapping input sequences to output sequences of different lengths, such as neural machine translation, text summarization, image …

Mar 12, 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 approach...

Apr 19, 2025 · The encoder-decoder architecture represents one of the most influential developments in deep learning, particularly for sequence-to-sequence tasks. This architecture has revolutionized …

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. …

Feb 13, 2025 · Explore the building blocks of encoder-decoder models with recurrent neural networks, as well as their common architectures and applications.

Apr 2, 2025 · Understanding the three main architectural patterns – Encoder-only, Decoder-only, and Encoder-Decoder – is crucial for anyone looking to leverage or simply understand modern NLP models.

In the attention mechanism, as in the vanilla encoder-decoder model, the vector c is a single vector that is a function of the hidden states of the encoder. instead of being taken from the last hidden state, it’s …

A hands-on guide to encoder-decoder models, Transformer internals, and step-by-step PyTorch code.

We’ll introduce the algorithm in sections Section 10.2, and in following sections give important components of the model like beam search decoding, and we’ll discuss how MT is evaluated, …