Encode 2D shapes into a 2-neuron bottleneck — compression and reconstruction error live.
An autoencoder compresses input data through a narrow bottleneck, forcing the network to learn a compact latent representation. Reconstruction error (MSE) measures information loss. With fewer bottleneck neurons, the network must learn the most essential features — the basis of dimensionality reduction and representation learning.