Autoencoders and Representation Learning Through Reconstruction

Unlabelled market snapshots, order-book states, or document embeddings often outnumber labelled examples. An autoencoder learns a compressed representation by training a network to reconstruct its own input through a narrow bottleneck, forcing the code to retain the information that matters and discard noise. That unsupervised code can feed downstream classifiers, anomaly screens, or dimensionality reduction — connecting deep representation learning to the density-based anomaly tools in classical unsupervised ML.