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.
5 lessons
Problem modeling, objectives, constraints, evolutionary methods, and metaheuristics for decision and search problems.
3 lessons
Connecting code, mathematical models, numerical experiments, data, and reproducible implementation.
3 lessons
Applied mathematics behind system behavior, rates, scoring, and engineering algorithms.
1 lessons
Search by concept or title, then narrow the results by category and level.
12 lessons
Convexity, positive semidefiniteness, and the role of the Hessian in convex QP.
A mathematics lesson connecting convex structure to the Convex QP Solver.
Vector geometry, similarity, and its role in search and AI.
A mathematics lesson connecting linear algebra to representation and relevance.
How a decision problem becomes variables, an objective, and constraints.
A fixture for teaching the mathematical language of decision and optimization problems.
From Lagrangians and KKT conditions to barrier methods and Convex QP.
A fixture for numerical error, step size, solution procedure, and interpretation.
From data representation and linear transforms to matrices inside model layers.
A fixture for a mathematical lesson with equations, examples, and AI context.
From one-variable derivatives to multivariable gradients and gradient descent.
A fixture for explaining derivatives, loss functions, and one gradient-descent step.
The core ideas and mathematics behind approximate search with GA and DE.
A fixture for introducing evolutionary and population-based optimization ideas.
From equation residuals to independent checks of numerical solutions.
A fixture for combining Python programming with numerical computation and analysis.
From matrix products and scores to softmax and weighted Value aggregation.
A mathematical explanation of attention using Q, K, V, and the core equations.
How limits and time windows become rates, intervals, and burst tolerance.
The mathematical ideas behind Token Bucket and GCRA, connected directly to RateLimitEngine.
From residuals and norms to condition numbers and numerical sensitivity.
A scientific-computing lesson about error, sensitivity, and validation.
Probability and inference foundations for statistical and machine-learning models.
A mathematics lesson about uncertainty, likelihood, and Bayes in AI.
This area is for conceptual, mathematical, and technical lessons, with real connections to projects, research, and solutions.
