What are autoencoders, and how are they used in unsupervised learning?

Autoencoders are neural network architectures used for unsupervised learning tasks such as dimensionality reduction, data denoising, and feature learning. They consist of an encoder network that compresses input data into a latent representation and a decoder network that reconstructs the original input from the latent representation. Autoencoders learn to capture meaningful features and patterns in the input data without requiring explicit labels.