What is an encoder decoder model?

What is an encoder decoder model?

Encoder Decoder is a widely used structure in deep learning and through this article, we will understand its architecture. In this post, we introduce the encoder decoder structure in some cases known as Sequence to Sequence (Seq2Seq) model.

In this post, we introduce the encoder decoder structure in some cases known as Sequence to Sequence (Seq2Seq) model. For a better understanding of the structure of this model, previous knowledge on RNN is helpful.

When do we use an encoder decoder model?

1-Image Captioning

Encoder decoder models allow for a process in which a machine learning model generates a sentence describing an image. It receives the image as the input and outputs a sequence of words. This also works with videos.

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ML output: ‘Road surrounded by palm trees leading to a beach’, Photo by Milo Miloezger on Unsplash

2-Sentiment Analysis

These models understand the meaning and emotions of the input sentence and output a sentiment score. It is usually rated between -1 (negative) and 1 (positive) where 0 is neutral. It is used in call centers to analyse the evolution of the client’s emotions and their reactions to certain keywords or company discounts.

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