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.
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.
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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To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own.