We have seen autoencoders in the deep learning chapter for unsupervised learning. Autoencoders utilize neural networks to perform non-linear dimensionality reduction. They represent data in a better way, by finding latent features in it using universal function approximators. Autoencoders try to combine or compress input data in a different way.

A sample representation using MLP is shown here: