Elliptic curve group law, discrete log behind ECDH, and the Frey-Ribet-Wiles argument that proved Fermat's Last Theorem, walked through with diagrams of y² = x³ + ax + b, point addition cases, and the modularity logic chain.
A projective geometry take on why a circle viewed in perspective and an ellipse drawn on paper behave differently, and why the ellipse's major axis doesn't line up with the circle's center.
A hub for the 5-article series that organizes math symbols in AI and LLM articles for reading, not solving. Covers equations, vectors and matrices, probability and statistics, derivatives, and gradient descent with backprop, plus a reading-order guide for different backgrounds.
Gradient descent, SGD and Adam, backpropagation, vanishing/exploding gradients with residual connections, and learning rate schedules — organized around what each piece is doing at a high level. The goal is reading training logs and model card numbers, not computing anything.
A minimum set of calculus for reading AI and LLM articles — d/dx, e, the chain rule, partial derivatives, and gradients. Focus on what the symbols are doing, not on solving the formulas.
A minimum set of probability and statistics for reading AI and LLM articles — conditional probability, cross-entropy, perplexity, and temperature are the main ones; rigorous Bayes and MLE derivations stay out of scope.
A minimum set of vectors and matrices for reading AI and LLM articles — the dot product and matrix product are the main two; determinants, inverses, and eigenvalues stay out of scope.
A minimum set of math for reading AI, LLM, and image-generation articles — the aim isn't to derive anything, just to recognize weighted sums, S-curves, probabilities, and the 'nudge toward the answer' step of training.
