Review Training

Training is the phase where a model learns from data by updating its internal parameters.

Training is the process that adjusts a model’s internal parameters using examples and an objective. Repeated updates make the model better at reducing its training loss for the selected task.

Training choices determine what behavior the model is rewarded for and what data it learns from. More training is not automatically better if the data or objective misses the real need.

A classifier processes a batch of labeled images, measures prediction loss, computes parameter changes, and repeats the cycle across many batches.

Training is not the same as giving a deployed model new information in a prompt. Prompt context affects a session; training changes model parameters.

Training changes the parameters; prompting changes the immediate context.

* [[model|Model]] * [[parameters|Parameters]] * [[dataset|Dataset]] * [[loss-function|Loss Function]]

* [Google for Developers, “Machine Learning Glossary”](https://developers.google.com/machine-learning/glossary) — provides established boundaries for core machine-learning vocabulary.

* Compare [[model|Model]], [[parameters|Parameters]], [[dataset|Dataset]], and [[loss-function|Loss Function]] to place Training in the surrounding system. * Follow the cited source material above for the method, standard, or research context behind this draft.