Autoencoders

Autoencoders are a type of artificial intelligence model used for learning efficient representations of data. They consist of two main parts: an encoder that compresses input data into a simpler format, and a decoder that reconstructs the original data from this compressed form. Think of it as a way for the computer to learn to summarize information, similar to how a student might condense notes for studying. Autoencoders are commonly used in tasks like image denoising, anomaly detection, and data compression, helping computers learn important patterns in datasets without needing explicit labels.