What is Autoencoder?

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data. The primary goal of an autoencoder is to learn a compressed, distributed representation (encoding) for a dataset. It typically works by compressing the input into a lower-dimensional code, and then reconstructing the original input from this code. Autoencoders are commonly used for dimensionality reduction, feature extraction, data denoising, and anomaly detection.