Machine Learning Enthusiast
Building machine learning systems from first principles.
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ENTRY — PERSONAL LOG
I approach machine learning the way an engineer approaches a bridge: not as a black box to invoke, but as a structure whose every beam and joint should be understood before it is trusted. Most of my work begins by tearing a well-known method down to arithmetic a loss function, a derivative, a loop and rebuilding it by hand until the magic disappears and only mechanism remains.
This site is kept the way a lab notebook is kept: dated, numbered, occasionally messy, always honest about what is finished and what is still on the bench.
Every project below is logged as an experiment: a hypothesis, a build, and a result. Status reflects the bench, not the résumé.
A from-scratch implementation of CART — splitting criteria, pruning,
and information gain derived and coded without a single line of
scikit-learn. Built to understand the arithmetic hiding under
.fit().
Field notes and long-form writing — the reasoning behind the code, kept for whoever reads the notebook after me.
Open to research collaboration, engineering roles, and honest conversations about how things actually work.