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t-SNE and UMAP are nonlinear dimensionality-reduction methods that preserve local neighborhoods to make high-dimensional data visible in 2D or 3D.
Gradient descent is a simple, repeatable way to move downhill on a loss surface by stepping in the opposite direction of the gradient.