[P] How a Deep Learning Library Enables a Model to Learn

A lot of us know that a model is “learning” when the loss goes down, and that the loss is computed from the prediction and the target. The less obvious part is what a deep learning library is actually doing internally to turn that loss into parameter updates that improve the model. I wrote a short post breaking that down: how the forward pass builds a computation graph, how loss.backward applies the chain rule across it, and how the resulting gradients become parameter updates via optimizer.step.