What are autoencoders and how are they used in deep learning?

Autoencoders are a type of neural network used for unsupervised learning. They consist of two main parts: an encoder, which compresses the input data into a lower-dimensional representation, and a decoder, which reconstructs the original data from this representation. Autoencoders are commonly used for tasks such as dimensionality reduction, denoising images, and anomaly detection, where learning efficient data representations is crucial.