Series
math-behind-neural-networks
4 parts, in order.
- 01
Jul 25, 2026
The Math Behind Neural Networks, Part 0 — Where Deep Learning Comes From
What a neural network is before any of the math: why deep learning had to exist, what every term actually means, and why a model must learn its own features. The one model the whole field grows from, built from zero.
- 02
Jun 9, 2026
The Leash That Makes AI Polite
How a 1951 statistics formula quietly keeps your chatbot from going feral. KL divergence, RLHF, and the tension between them.
- 03
May 24, 2026
The Math Behind Neural Networks, Part 1.2 — The Mathematics of Learning
The derivative, derived by hand from MSE, and gradient descent built on top of it. Where the 2/n comes from, why we subtract the gradient, and a full worked training example.
- 04
May 9, 2026
The Math Behind Neural Networks, Part 1.1 — The Mathematical Foundations
Slope, linear regression, and the error function, built from the ground up. Why we divide by n, why we square the error, and why a loss value alone cannot tell you how to improve.