Glossary

Supervised Fine-Tuning is a vital technique in machine learning, utilized to elevate the output of pre-trained models, by training them on bigger datasets before fine-tuning them on smaller, more definite ones.

In Supervised Learning, a model is trained using labeled data to predict or determine unknown outputs. It uses input-output pairs in training data.

A Support Vector Machine (SVM) is an algorithm used for supervised learning tasks, specifically for classification and regression. Trained on labeled data, SVMs find the best separating hyperplane.