Question 1

What is an autoencoder and how does it detect anomalies?

Accepted Answer

An autoencoder is a neural network trained to reconstruct its own input through a narrow bottleneck layer. Trained only on normal sensor readings, it reconstructs normal patterns with low error and unfamiliar patterns with high error, which is used as the anomaly score.

Question 2

How is this network trained in the browser?

Accepted Answer

Every frame the simulation generates fresh synthetic normal sensor data, runs a forward pass, computes mean-squared reconstruction error, and runs a genuine backward pass using the chain rule to update every weight and bias via gradient descent — no ML library involved.

Question 3

Why does the network only train on normal data?

Accepted Answer

Because real anomalies are rare and varied, autoencoder anomaly detection only needs abundant normal examples; anomaly detection then falls out as an unsupervised by-product of learning to compress normal operation well.