**Autoencoder**

An Autoencoder is a Neural Network model whose goal is to predict the input itself, typically through a “bottleneck” somewhere in the network. ^{1} By introducing a bottleneck, we force the network to learn a lower-dimensional representation of the input, effectively compressing the input into a good representation. Autoencoders are related to PCA and other dimensionality reduction techniques, but can learn more complex mappings due to their nonlinear nature. A wide range of autoencoder architectures exist, including Denoising Autoencoders, Variational Autoencoders, or Sequence Autoencoders.

**Sources**

“Deep Learning Glossary.”

*WildML*, 8 Sept. 2017, www.wildml.com/deep-learning-glossary/ (1)