Prototypical Networks perform few-shot learning by computing class prototypes in learned embedding space. Core idea: Examples from same class should cluster together. Represent each class by mean embedding of its examples (prototype). Classify by distance to prototypes. Algorithm: Encode support examples → compute prototype per class (mean embedding) → encode query → compute distances to all prototypes → softmax over negative distances for classification. Distance function: Typically Euclidean or cosine distance. Euclidean has theoretical justification (Bregman divergences). Training: Episodic training matching test-time setup. Sample N-way K-shot tasks from training classes. Simplicity advantage: No learned comparison function (unlike Matching Networks), just mean and distance. Fewer parameters, less overfitting. Extensions: Task-conditioned prototypes, transductive inference, hierarchical prototypes. Zero-shot variant: Use class name embeddings as prototypes. Performance: Competitive with more complex meta-learning methods, especially on standard benchmarks. Simple, elegant, widely adopted baseline for few-shot classification.