An autoencoder is a type of artificial neural network that is used to learn efficient representations of data. The main goal of an autoencoder is to reduce the dimensionality of the input data, i.e. compress it into a smaller feature space, and then reconstruct the original output data from this compressed representation.
An autoencoder consists of two main parts: the encoder and the decoder. The encoder takes the input data and transforms it into a compressed representation in feature space. The decoder takes this compressed representation and uses it to reconstruct the original output data.
The idea behind an autoencoder is that, by forcing the model to learn an efficient representation of the data, it is also forcing the model to identify the most important features of the input data. Therefore, autoencoders are useful for dimensionality reduction, data denoising and anomaly detection.
Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
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