An autoencoder is a type of artificial neural network used for unsupervised learning. It consists of two main parts: an encoder that compresses input data into a lower-dimensional representation, and a decoder that reconstructs the original data from this compressed form. The goal is to learn efficient representations of the data, often for tasks like dimensionality reduction or noise reduction. During training, the autoencoder minimizes the difference between the input and the reconstructed output, known as the reconstruction error. This process helps the model capture important features of the data while ignoring noise, making it useful in various applications such as image processing and anomaly detection.