omething that computers and classical engines do normally, so you might ask, what is special about neural networks? The answer is that the architecture of the network and the types of calculations are designed to be very calculus-friendly, and often take biological structural cues to create a deterministic, stable and fast way to <em>train</em> the entire network at once. Like a teacher in a classroom, we can give the network a problem to work on, compare its answer to the real one, and use calculus to upda