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...

Aug 16, 2024 · The Encoder-Decoder architecture is a fundamental concept in machine learning, especially in tasks involving sequences such as machine translation, text summarization, and image captioning.

Mar 1, 2025 · Autoencoders consists of two components: Encoder: This compresses the input into a compact representation and capture the most relevant features. Decoder: It reconstructs the input data from this compressed form to make it as similar as possible to the original input.

Jan 12, 2024 · What’s Encoder-Decoder Architecture & How does it work? The encoder-decoder architecture is a deep learning architecture used in many natural language processing and computer vision applications. It consists of two main components: an encoder and a decoder.

Jun 17, 2023 · However, the main difference is that encoders are designed to learn embeddings that can be used for various predictive modeling tasks such as classification. In contrast, decoders are designed to generate new texts, for example, answering user queries.

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.

Oct 1, 2024 · Encoder-decoder is a type of neural network architecture used for sequential data processing and generation. 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.

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, 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...

What Are Encoders and Decoders? Encoder: A component that converts input data into a compressed, meaningful representation (often called a context vector or latent space). Decoder: A component that reconstructs output data from the encoder’s representation, often translating it into a different format or language.