Fine-Tuning

Few-Shot Learning enables AI models to quickly adapt to new tasks using only a small number of training examples, mimicking human ability to learn efficiently. This approach reduces the need for extensive data collection and training time, making AI more flexible for real-world applications. It leverages prior knowledge and advanced algorithms to generalize from minimal data effectively.