👉 Feof computing, or "few-shot learning," is a machine learning paradigm that focuses on training models to generalize from very few examples, typically just a handful or even a single instance per class. Unlike traditional machine learning, which often requires large datasets to achieve good performance, few-shot learning aims to enable models to learn new tasks or recognize new classes with minimal data. This is particularly useful in scenarios where collecting extensive labeled data is impractical or costly, such as in niche applications or rapidly evolving domains. By leveraging transfer learning and meta-learning techniques, few-shot computing models can quickly adapt to new tasks by building on previously learned knowledge, making them highly efficient and versatile in real-world applications.
