Autoencoders are a type of artificial neural network used for learning efficient representations of data, typically for the purpose of dimensionality reduction or denoising. These networks are unsupervised learning models that rely on the principle of data encoding and decoding. An autoencoder aims to learn a compressed, distributed representation of the dataset, typically for the purpose of dimensionality reduction.
Two main components of autoencoder are an encoder and a decoder. The encoder network takes in the input data and compresses it into a lower-dimensional representation, also known as the coded version or bottleneck, which carries the most important features of the input data. The decoder network then reconstructs the original data from this compressed representation as accurately as possible. The entire network is trained by minimizing the difference between the original input and the reconstructed output, commonly referred to as reconstruction error.
Autoencoders are valuable tools in the world of deep learning and artificial intelligence, allowing us to deal with high dimensional data effectively. While their most common use is in dimensionality reduction, they are also used in a plethora of other applications like anomaly detection, image denoising, and generative modeling. The autoencoders’ ability to learn data representations automatically makes them a powerful instrument in various fields, ranging from computer vision to natural language processing.