Autoencoders

Autoencoders are a type of artificial neural network used for unsupervised learning. They consist of two main components: an encoder that compresses the input data into a lower-dimensional representation, and a decoder that reconstructs the original data from this compressed form. The primary goal of an autoencoder is to learn efficient representations of the input data, often for the purpose of dimensionality reduction or feature extraction. Common use cases include image denoising, anomaly detection, and data compression. By training on a dataset, autoencoders can capture the underlying structure and patterns, making them valuable in various applications within machine learning and data science.