What is Few-Shot Learning, and how does it enable models to learn from a limited amount of data? How does Few-Shot Learning differ from Zero-Shot and traditional supervised learning approaches? What techniques are commonly used to implement Few-Shot Learning in machine learning models? What are the advantages and challenges of using Few-Shot Learning in real-world applications? In which domains is Few-Shot Learning most effectively applied?