t-Distributed Stochastic Neighbor Embedding reduces high-dimensional data to 2D while preserving local structure.
Cost = KL(P || Q)
Key parameter: Perplexity controls the effective number of neighbors considered.
Watch as the algorithm iteratively minimizes the KL divergence to separate clusters.