#69 DR. THOMAS LUX - Interpolation of Sparse High-Dimensional Data

Today we are speaking with Dr. Thomas Lux, a research scientist at Meta in Silicon Valley.

In some sense, all of supervised machine learning can be framed through the lens of geometry. All training data exists as points in euclidean space, and we want to predict the value of a function at all those points. Neural networks appear to be the modus operandi these days for many domains of prediction. In that light; we might ask ourselves — what makes …

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