Numerical Computing and Stability Questions

Focuses on the numerical aspects of machine learning algorithms and how to ensure stable computation. Topics include floating point precision and rounding errors, underflow and overflow, conditioning and ill posed problems, numerically stable matrix factorizations and solvers, gradient scaling and clipping, mixed precision considerations, and diagnostics for identifying numerical issues.