Auto-encoder

An autoencoder is a type of neural network that is used for unsupervised learning. It consists of two main parts: an encoder and a decoder. The encoder is responsible for transforming the input data into a more compact representation, while the decoder is responsible for transforming the compact representation back into the original input data.

Autoencoders are typically used for dimensionality reduction, data compression, and feature extraction. They can be trained on a large dataset of unlabeled data, and then used to extract meaningful features from new data.

Autoencoders have been used in a variety of applications, including image and speech recognition, anomaly detection, and data denoising. They are also commonly used in deep learning architectures, such as convolutional neural networks and recurrent neural networks.