An autoencoder is a type of neural network designed to learn efficient representations of data, typically for the purpose of dimensionality reduction or feature learning. It consists of two main components: the encoder and the decoder. The encoder compresses the input data into a lower-dimensional latent representation, while the decoder reconstructs the original data from this compressed form. This process allows the autoencoder to learn the underlying structure of the data without requiring labeled outputs, making it an unsupervised learning method ¹⁵.

Autoencoders are commonly used in various applications, including image denoising, anomaly detection, and data compression. They work by minimizing the reconstruction error, which is the difference between the input and the output of the network. By training on a dataset, the autoencoder learns to capture the most important features of the data, effectively filtering out noise and irrelevant information ⁴⁵.

In practice, autoencoders can be implemented using frameworks like Keras or PyTorch, and they can be tailored to specific tasks by adjusting the architecture and training parameters ¹².