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.
6 items
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 mathematics lesson connecting vectors and matrices to data representation and AI model layers.
From one-variable derivatives to multivariable gradients and gradient descent.
A lesson connecting derivatives and gradients to loss functions and gradient-based learning.
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.
A guided explanation of the 2017 Transformer paper, from the sequence-transduction problem to scaled dot-product attention and the encoder-decoder architecture.
A paper explainer that keeps the original source authoritative while unpacking its main ideas, equations, and engineering implications.
This area is for conceptual, mathematical, and technical lessons, with real connections to projects, research, and solutions.
