Autoencoder

An autoencoder is a type of neural network that learns to compress data into a smaller, simplified representation and then reconstruct it back to its original form. Think of it like summarizing a complex document into key points, then expanding it back to full detail. This process helps the network identify important features and patterns within the data. Autoencoders are often used for tasks like noise reduction, data compression, and feature extraction, making large data more manageable and meaningful for various applications.