About Me
I'm an AI developer and educator with a deep foundation in mathematics — not just as a tool, but as a way of thinking that informs how I design and build intelligent systems. My journey began with a passion for problem-solving and abstract reasoning, which led me to study a broad spectrum of mathematical fields. This includes core areas essential to AI, such as linear algebra, probability theory, statistics, and optimisation, as well as foundational computer science topics like cryptography, data structures, and algorithms. I’ve also explored more theoretical branches of mathematics — including real and complex analysis, graph theory, combinatorics, number theory, and abstract algebra.
This breadth of experience gives me a distinct advantage in AI development. I don’t just apply algorithms — I understand their underlying structure, theoretical foundations, and practical limitations. Whether it’s designing neural network architectures, fine-tuning models, or developing efficient pipelines, my mathematical background shapes the way I think, build, and teach.
Today, my focus is on making AI more accessible. I create tutorials, tools, and educational resources to help developers at all levels deepen their understanding of artificial intelligence — not just how to use it, but how it works beneath the surface. Whether you're taking your first steps into AI or exploring the mathematical theory behind advanced models, my goal is to help you bridge the gap between theory and practice with clarity, precision, and confidence.