# Auto Encoder

Autoencoders are a class of neural networks designed for unsupervised learning and representing features in a smaller space. They consist of an encoder and a decoder, intending to learn the input data's compressed representation (encoding).  We leverage this architecture to generate synthetic data.

SDGnE package contains three Autoencoder models:

<table data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td></td><td>Balanced Autoencoder</td><td></td><td><a href="auto-encoder/balanced-autoencoder">balanced-autoencoder</a></td></tr><tr><td></td><td>Heavy Decoder Autoencoder</td><td></td><td><a href="auto-encoder/heavy-decoder-autoencoder">heavy-decoder-autoencoder</a></td></tr><tr><td></td><td>Single Encoder Autoencoder</td><td></td><td><a href="auto-encoder/single-encoder-autoencoder">single-encoder-autoencoder</a></td></tr></tbody></table>