In artificial intelligence and neural networks, the encoder is the part of a model whose job is to process input data and convert it into a condensed, numerical form. This internal representation, often referred to as a context vector or latent space, captures the essential properties and meaning of the original information, whether it is text, an image or a sound. The encoder’s goal is to create an information-rich summary that is efficient for further processing, for example for classification or for handing over to a decoder within more complex architectures.
Imagine reading a whole article and then trying to sum up its main idea in a single sentence for a friend. That is exactly what an encoder does. It takes a large quantity of information at the input and “thinks it through” so as to produce a short but pithy summary in a language the computer understands. It can then use this compact package of the most important information to decide what the article was about, or pass it on for further processing.