A single, complete introduction to graph neural networks — the two earlier write-ups merged into one, losing none of the figures or interactive simulations. It runs in two parts: Part 1, explained simply, builds the anatomy (encoder → message-passing layers → readout → training); Part 2, a working introduction, covers the main architectures, the tasks, the failure modes, and where graph learning fits vehicle health.
Read it
Both parts live at one URL, with a jump-nav between them. Part 1 is the plain anatomy with the message-passing demo; Part 2 is the working introduction with the architecture comparisons and the message-passing sandbox.
← Back to Autonomy