Autoencoders

Autoencoders are designed for unsupervised learning using artificial neural networks for tasks such as data compression, feature extraction, and reconstruction. They can reconstruct data by learning efficient encoding from the original input. This process can be optimized by minimizing the difference between the original input and the reconstructed output. A loss function is applied, which can include cross-entropy, mean absolute error, and mean squared error.