4 docs tagged with "concepts"

Concepts are named requirements for template arguments that replace SFINAE and enable_if with readable constraints. Requires expressions are the building blocks that check if code compiles at compile-time.

Every model so far in this curriculum has learned from labelled examples — here is the input, here is the correct output. Reinforcement learning removes the labels entirely: an agent takes actions, receives only a scalar signal for how well it did, and has to figure out which of its own past decisions deserve the credit.

Every model in the classical ML and deep learning sections so far predicts a label or value from an input. A generative model asks a different, harder question: can you produce a new example that looks like it came from the same distribution as the training data? Answering "yes" requires learning something classifiers never need — an actual model of what the data looks like.

Traditional software is a set of rules a person wrote down specify the objective, let optimisation find the rule.