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.
5 lessons
Clear filtersVector geometry, similarity, and its role in search and AI.
A mathematics lesson connecting linear algebra to representation and relevance.
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.
From matrix products and scores to softmax and weighted Value aggregation.
A mathematical explanation of attention using Q, K, V, and the core equations.
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.
