Autoencoders
Autoencoders are a type of artificial neural network used for unsupervised learning. They work by attempting to reconstruct the input data at the output, typically by compressing the data into a lower-dimensional representation and then expanding it back to the original dimensions. This process helps the model learn the most important features of the data and can be used for tasks such as data denoising, dimensionality reduction, and anomaly detection.