Think of an artist who is asked to draw a detailed landscape, but first, they must describe it in a single, short sentence. That sentence has to capture the most essential parts of the scene. Later, a different artist must recreate the landscape using only that one-sentence description. The better the sentence, the closer the new drawing will be to the original.

An autoencoder works in a similar way. It's a type of neural network that learns to create a compressed summary of data and then reconstruct the original data from that summary. This is a form of unsupervised learning, meaning the network learns patterns directly from the data itself, without needing any labels or answers provided by humans.

This ability makes autoencoders useful for tasks like data compression (similar to creating a ZIP file of an image), removing noise from data, or identifying important features within a dataset.

Every autoencoder has two main components: an encoder and a decoder.