Mathematics & computational learning
Lessons that explain the mathematics used in real projects, from concept and equations to worked examples and implementation.
Linear algebra, calculus, probability, statistics, and optimization with a focus on where the mathematics appears in AI.
6 items
Problem modeling, objectives, constraints, evolutionary methods, and metaheuristics for decision and search problems.
3 items
Connecting code, mathematical models, numerical experiments, data, and reproducible implementation.
4 items
Applied mathematics behind system behavior, rates, scoring, and engineering algorithms.
1 items
Search by concept or title, then narrow the results by category and level.
4 items
Clear filtersFrom Lagrangians and KKT conditions to barrier methods and Convex QP.
A numerical-computing lesson connecting Lagrangians, KKT conditions, barrier methods, and Convex QP.
From equation residuals to independent checks of numerical solutions.
A scientific-computing lesson connecting Python implementation with residuals, numerical answers, and independent checks.
From residuals and norms to condition numbers and numerical sensitivity.
A scientific-computing lesson about error, sensitivity, and validation.
A guided reading of a compact autograd engine and neural-network library, using it to make reverse-mode differentiation concrete.
A project explainer showing how a small public codebase can make computational graphs, local derivatives, and backpropagation easier to inspect.
This area is for conceptual, mathematical, and technical lessons, with real connections to projects, research, and solutions.
