Why training a model is just walking downhill on a loss landscape — and why that walk can get stuck.
"Training" a machine learning model sounds mysterious, but the core algorithm underneath almost all of it is mechanically simple: measure how wrong the model currently is, figure out which direction makes it less wrong, and take a step that way. Repeat thousands or millions of times. That algorithm is gradient descent, and nearly everything interesting about why training is fast, slow, stable, unstable, or stuck comes down to two things — how big a step you take, and what the shape of the landscape you're walking on actually looks like.