The model learns from these examples by adjusting its internal parameters to minimize errors and improve performance on the training data. The goal is for the model to generalize well to new, unseen data, allowing it to make accurate predictions or take appropriate actions.
Training examples can be in various forms, such as text, images, audio, or videos, depending on the task at hand. The quality and diversity of these examples play a crucial role in determining the performance and reliability of the trained AI model.