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.

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.

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.

Nov 17, 2024 · Deep Dive into the architecture & building real-world applications leveraging NLP Models starting from RNN to Transformer. · 1. Introduction. · 2. Understanding Sequence Modeling. · 3....

This is the story of the Encoder-Decoder architecture, the neural blueprint behind modern NLP breakthroughs like Transformers, GPT, and BERT spin-offs.

Oct 1, 2024 · Much machine learning research focuses on encoder-decoder models for natural language processing (NLP) tasks involving large language models (LLMs). 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 ...

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 11, 2025 · The encoder-decoder architecture is a fundamental framework in natural language processing (NLP) that facilitates the handling of various tasks through a structured approach. This architecture is primarily composed of three key components: the encoder layer, the decoder layer, and the loss and optimization mechanisms.

Dec 16, 2024 · Decoder architecture. A decoder is meant to generate an output sequence therefore it also consists of a LSTM/RNN cell which unfolds over time. The initial Ht and Ct for decoder is the final representation of Ct and Ht in encoder i.e. the context vector.