Autoencoders
Autoencoders are neural networks designed to learn efficient data codings in an unsupervised manner. They achieve this by encoding input data into compact representations, called bottlenecks, and then reconstructing the original input from these representations. This process forces the network to prioritize and retain only the most essential features of the input data, thereby achieving dimensionality reduction. Autoencoders have a wide range of applications in various domains including image processing, anomaly detection, and generative modeling. In the context of deep learning algorithms, autoencoders serve as an effective tool for feature extraction and learning robust representations, contributing to advancements in technology and artificial intelligence.