autoencoders
An autoencoder is a type of neural network used primarily for unsupervised learning. Its goal is to learn an efficient encoding of input data, often for the purposes of dimensionality reduction or feature extraction. The structure of an autoencoder typically involves two main components:
Encoder: The encoder maps the input data (e.g., an image, text, etc.) into a lower-dimensional latent space (i.e., a compressed representation of the data). It essentially learns how to "encode" the input into a smaller vector that retains the most important features of the data.
Decoder: The decoder takes this encoded representation and reconstructs the input data from it. The network is trained to minimize the difference between the original input and the reconstructed output (i.e., the reconstruction error).