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Explore articles on AI/ML, Robotics, Microcontrollers, and Automation

Understanding SVMs
ML Fundamentals5 min read

The Kernel Trick in Support Vector Machines (SVMs)

For the longest time I believed Support Vector Machines were maximum-margin linear classifiers with a fancy name. I could recite the objective function, draw the margin and the support vectors, and still feel smug. How is it finding a hyperplane in 2D when no straight line can ever separate these spirals? That confusion ended with the simplest sentence in ML: We never actually go to the high-dimensional space, we only pretend we do.

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Understanding Bayes Theorem
ML Fundamentals8 min read

Revisiting Bayes' Theorem: The Quiet Engine of AI

Bayes’ Theorem is everywhere in machine learning, yet strangely hard to feel intuitively. Even after years in AI and computer vision, I still found it confusing. A centuries-old idea powering today’s models, but never explained in a way that truly clicks. This post is a simple, human walkthrough of Bayes: what it means, why it matters, and how it quietly runs beneath everything we build in modern AI.

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