First article in the BetterExplained series on this blog — not about formulas, but about how to understand them.
Learn Right, Not Rote.
Math is no more about equations than poetry is about spelling.
Formulas and rules are tools. Without a sense of why something works, they become symbols that are easy to forget and hard to apply. In machine learning this shows up everywhere: gradient, matrix, integral — the meaning slips away if there is no intuition behind them.
BetterExplained
BetterExplained is Kalid Azad’s site of intuitive math explanations. The material is built from images and meaning toward notation, not the other way around.
The usual pattern: first what is going on (geometry, analogy, story), then symbols. That makes it easier to connect school math with linear algebra, calculus, and probability for AI.
What is useful for this series
- Derivatives and integrals — rate of change and accumulation before the formulas; good prep for derivatives and the circle area example.
- Exponents and e — why
e^xbehaves as it does; useful for softmax, loss, and growth in models. - Vectors and dot products — geometric meaning before matrix algebra.
- Euler’s formula — one intuition linking exponentials, sin/cos, and complex numbers.
You do not need to read everything: pick the topic that still feels opaque in a textbook or an ML article.
How to use it with this blog
- Here — why the math matters for AI and short ML-oriented write-ups.
- On BetterExplained — deeper intuition on classic topics.
- Return to the formulas with a mental picture — articles, code, and lectures become easier.
Next in the series: BetterExplained — Part 2 — Arithmetic.