ml

Parameters are learned by the model during training. Hyperparameters are set by humans before training. Mixing them up causes confused debugging.

Parameters is the umbrella term for every learnable number in a model — weights plus biases. In practice, people use the two words interchangeably.

An algorithm is a procedure. A model is the trained result of running that procedure on data. Same algorithm can produce many models.

Training is how a model learns from data. Inference is how it applies what it learned to new inputs. Different costs, hardware, and time scales.

Ship personalized recommendations using embeddings, collaborative filtering, and LLM re-ranking — from cold start to production scale.