Autoencoders

Autoencoders are a type of neural network that work by encoding input data into a lower-dimensional representation (latent space) and then decoding it back to its original form, effectively acting as a data compression and dimensionality reduction tool and offering efficient ways to analyze and generate data. Autoencoders are trained using unsupervised learning without explicit labels by minimizing the difference between the original input and the reconstructed output. They are versatile tools with the ability to learn from unlabeled data, automatically extract features without explicit instruction, and capture nonlinear relationships within data. The architecture of the autoencoder is described, and the roles played by the various layers constituting its structure are explained. The training process of the autoencoder algorithm is elaborated, and the use of a trained autoencoder algorithm is discussed.