An autoencoder is a type of artificial neural network used for unsupervised learning. It is designed to learn a compressed representation of input data by encoding the data into a lower-dimensional space and then decoding it back to its original form. This process helps in reducing the dimensionality of the data and capturing the most important features for reconstruction. Autoencoders have applications in various fields such as image and speech recognition, anomaly detection, and data compression. They are often used for tasks like image denoising, feature extraction, and dimensionality reduction.