Oct 17, 2021 · How does an Encoder-Decoder work and why use it in Deep Learning? The Encoder-Decoder is a neural network discovered in 2014 and it is still used today in many projects. It is a fundamental pillar of Deep Learning. It is found in particular in translation software.

Mar 11, 2021 · Encoder-Decoder models were originally built to solve such Seq2Seq problems. In this post, I will be using a many-to-many type problem of Neural Machine Translation (NMT) as a running example....

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.

Mar 10, 2021 · There are three main blocks in the encoder-decoder model, The Encoder will convert the input sequence into a single-dimensional vector (hidden vector). The decoder will convert the hidden...

Jan 12, 2024 · In the field of AI / machine learning, the encoder-decoder architecture is a widely-used framework for developing neural networks that can perform natural language processing (NLP) tasks such as language translation, text summarization, and question-answering systems, etc which require sequence-to-sequence modeling.

Feb 13, 2025 · Encoder-Decoder models and Recurrent Neural Networks are probably the most natural way to represent text sequences. In this tutorial, we’ll learn what they are, different architectures, applications, issues we could face using them, and what are the most effective techniques to overcome those issues.

Aug 7, 2019 · In this post, you will discover the two seminal examples of the encoder-decoder model for neural machine translation. After reading this post, you will know: 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.

Oct 1, 2024 · In deep learning, the encoder-decoder architecture is a type of neural network most widely associated with the transformer architecture and used in sequence-to-sequence learning. Literature thus refers to encoder-decoders at times as a form of sequence-to-sequence model (seq2seq model).

Jun 24, 2023 · An encoder-decoder is a type of neural network architecture that is used for sequence-to-sequence learning. It consists of two parts, the encoder and the decoder. The encoder processes an input...

Aug 14, 2019 · The Encoder-Decoder LSTM is a recurrent neural network designed to address sequence-to-sequence problems, sometimes called seq2seq. Sequence-to-sequence prediction problems are challenging because the number of items in …