Autoencoder
Autoencoders are a type of artificial neural network used in machine learning for unsupervised learning of efficient codings. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction or denoising. It consists of two parts: an encoder that maps the input data to a hidden representation, and a decoder that maps this hidden representation back to the input space. The goal is to minimize the difference between the original input and the reconstructed output, thereby learning a compact and informative representation of the input data. Autoencoders have found applications in various fields such as image processing, natural language processing, and anomaly detection. They can be seen as a specific type of artificial neural network that is trained to reconstruct its inputs, making them a useful tool for unsupervised learning tasks within the broader context of artificial intelligence algorithms and machine learning methods.