Linear Algebra Through Stable Solvers
Defend a low-rank reconstruction of a small matrix using singular values, measured error and a rank-selection rationale.
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Defend a low-rank reconstruction of a small matrix using singular values, measured error and a rank-selection rationale.
Explain a saddle point with a Hessian example and defend the gradient checks in a reusable calculus notebook.
Defend a precision and stopping policy using measured accuracy, memory and computation costs.
Present a tested feature-normalization module and defend its dtype, shape, copy and constant-column policies.
Compute and plot predictive distributions under two stated priors on the same data; justify the uncertainty change without interpreting likelihood as posterior probability.
Defend the schema and error policy, demonstrate one repaired bug, and hand the project to a reviewer who can reproduce its outputs.
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