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 feature extraction. Autoencoders are commonly used in various applications, including image denoising, anomaly detection, and data compression. By training on large datasets, they can capture important patterns and structures, making them valuable tools in fields like computer vision and natural language processing.