What is Autoencoders?

Autoencoders are a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning). An autoencoder is trained to reconstruct its input. It consists of two main parts: an encoder that compresses the input into a latent-space representation, and a decoder that reconstructs the input from this representation. By forcing the network to compress the data, it learns to extract the most relevant features. Autoencoders are commonly used for dimensionality reduction, feature extraction, anomaly detection, and generative modeling.