t-SNE
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for dimensionality reduction and data visualization, particularly effective for exploring high-dimensional datasets. It converts similarities between data points into joint probabilities and minimizes the Kullback-Leibler divergence between these probabilities in high and low-dimensional spaces, producing intuitive 2D or 3D visualizations that reveal clusters and patterns. It is widely used in fields like bioinformatics, natural language processing, and image analysis to understand complex data structures.
Developers should learn t-SNE when working with high-dimensional data (e.g., from neural networks, genomics, or text embeddings) to visualize clusters, outliers, and relationships that are not apparent in raw data. It is especially useful for exploratory data analysis, model debugging, and presenting insights to non-technical stakeholders, as it often produces more interpretable plots than alternatives like PCA for non-linear structures. However, it is computationally intensive and not suitable for large datasets without optimizations.