8 articles & lessons
Mathematics
Articles and learning notes on mathematics.
Statistics — CLT, Hypothesis Testing, Confidence Intervals
The three ideas that let you turn noisy data into defensible claims: the Central Limit Theorem, p-values, and confidence intervals — with the mistakes that get careers cancelled.
50 min readProbability — Random Variables, Distributions, Expectation
Uncertainty, quantified: random variables, the distributions you'll actually meet (Bernoulli, binomial, normal, exponential, Poisson), expectation, variance, and Bayes' rule.
55 min readCalculus II — Gradients & Gradient Descent from Scratch
The one algorithm behind all of ML: gradients as the ‘uphill direction’ in n dimensions, SGD as the workhorse, and the learning-rate/momentum knobs that decide whether you converge or explode.
55 min readCalculus I — Derivatives & Chain Rule
Rate of change is the heart of learning: the derivative as slope, symbolic vs numeric differentiation, and the chain rule that powers every neural net.
55 min readLinear Algebra II — Matrices, Transforms, Eigenvalues
Matrices as linear transformations, matrix multiplication as function composition, and eigenvalues as the axes a transform respects — the shape of every neural net.
55 min readLinear Algebra I — Vectors, Dot Product, Geometry
The language ML speaks: vectors as arrows and lists of numbers, the dot product as similarity, norms as length, and cosine similarity as ‘how alike?’
55 min readDiscrete Math — Sets, Logic, Combinatorics, Graphs
The vocabulary of computer science: sets, boolean logic, counting arguments, and graph theory — the four discrete-math tools you'll use in every subsequent session.
55 min readBig-O Notation — Reasoning About Scale
How to talk about performance without measuring: growth rates, worst/avg/amortised cost, and the constant-factor traps that make theory disagree with reality.
55 min read