An Autoencoder is an unsupervised learning algorithm used for data compression and feature extraction in deep learning models. It works by compressing the input data into a lower-dimensional representation and then reconstructing the data from the compressed representation.

Autoencoders consist of an encoder network that maps the input data to a lower-dimensional representation and a decoder network that maps the lower-dimensional representation back to the original data.

The goal of an Autoencoder is to minimize the difference between the input data and the reconstructed data, typically measured by a loss function such as mean squared error. Autoencoders can be used for tasks such as denoising and anomaly detection by reconstructing input data that is corrupted or different from the training data.

They have also been used in computer vision and natural language processing applications to learn meaningful representations of images and text data, respectively. Autoencoders are a powerful tool in the field of deep learning and have many potential applications in unsupervised learning, data compression, and feature extraction.