An autoencoder is a type of artificial neural network designed for unsupervised learning, primarily used for feature extraction and dimensionality reduction. It consists of two main components: the encoder, which compresses input data into a lower-dimensional representation, and the decoder, which reconstructs the original data from this compressed form. By learning to encode and decode data, autoencoders can capture essential features and patterns, making them valuable for tasks such as image reconstruction, anomaly detection, and data compression. Their ability to work with unlabeled data distinguishes them from other machine learning techniques, such as supervised learning methods.