stacked autoencoder

Stacked autoencoder is a type of artificial neural network that is used for unsupervised learning. It consists of multiple layers of autoencoders that are stacked on top of each other, creating a deep neural network. In each layer, the input data is compressed and then reconstructed to its original form, with the aim of minimizing the reconstruction error. The output of one layer is fed as input to the next layer, allowing the network to learn increasingly complex representations of the input data. Stacked autoencoders have been successfully used in various tasks such as image recognition, natural language processing, and anomaly detection.