Notes on control and machines that learn.
A working notebook at the edge where classical control theory meets modern machine learning — written for engineers who want the math, the code, and the intuition in the same place.
Topics
01 / Explore01 — 2 posts
Control Systems
PID, state-space, frequency domain, stability — the math that keeps things in line.
→02 — 2 posts
Motor Control
DC, BLDC, stepper. FOC, commutation, encoders, current loops.
→03 — 2 posts
Machine Learning
From linear regression to gradient boosting — the classical toolkit.
→04 — 2 posts
Neural Networks
Architectures, training dynamics, and the bits underneath the hype.
→05 — 2 posts
Reinforcement Learning
Agents, rewards, policies. Where control theory and ML meet.
→Recent writing
02 / Latest-
Apr 19, 2026Machine LearningA working Bayesian optimization loop you can run in your browser. Watch Expected Improvement, UCB, and PI compete to find the minimum of a deceptive benchmark function — and rea...
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Apr 19, 2026Machine LearningClick, slide, and watch the posterior update. A working intuition for Gaussian Processes — from the one-sentence definition through the Cholesky math and the honest O(n³) scalin...