A Denoising Autoencoder is a type of artificial neural network used for unsupervised learning. Its primary purpose is to learn efficient representations of data by reconstructing input data that has been corrupted or "noised." During training, the model receives a partially corrupted version of the input and aims to predict the original, clean data. This process helps the model learn important features and patterns in the data. The architecture of a denoising autoencoder consists of two main parts: an encoder that compresses the input into a lower-dimensional representation, and a decoder that reconstructs the original input from this compressed form. By focusing on recovering the original data, the model becomes robust to noise and can be useful in various applications, such as image processing and data compression.