t-Distributed stochastic neighbor embedding

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a powerful dimensional reduction method in machine learning. It permits data scientists to visualize high-dimensional data in a low-dimensional environment, such as a scatter plot in two dimensions. t-SNE functions by minimizing the divergence between two distributions: one that measures pairwise similarities of the input data and one that measures pairwise similarities of the low-dimensional representation of the data. This enables t-SNE to preserve the local data structure while revealing global trends. t-SNE has been utilized to view a vast array of data, including images of handwritten numbers and intricate networks of neurons in the brain. It has aided in discovering previously difficult-to-detect buried data structures and has proven to be an effective tool for academics in various sectors.