 Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name.Several variants exist to the basic model, with the aim of forcing the learned representations of the input to assume useful properties.Examples are the regularized autoencoders ("Sparse", "Denoising" and "Contractive" autoencoders), proven effective in learning representations for subsequent classification tasks, and "Variational" autoencoders, with their recent applications as generative models.