This repository provides clean, tested implementations of few-shot learning research papers. It includes code for Prototypical Networks and MAML-style meta-learning algorithms trained on standard few-shot benchmarks (Omniglot, miniImageNet). The project supports running experiments to reproduce published results and includes accompanying Medium articles explaining the theory and implementation details.