Training data is the collection of examples (text, images, code, etc.) used to teach a machine learning model. The model learns patterns, associations, and logic from this data. The quality, diversity, and size of the training dataset are the most critical factors determining a model's performance and bias.
Fundamental concept in supervised and self-supervised learning.
Massive datasets like Common Crawl and The Pile have enabled the rise of Large Language Models.