❤️ My Love for Mathematics

My journey with mathematics began in childhood — long before I learned to code. I’ve always been fascinated by patterns, logic puzzles, and number games. Unlike many, I never saw math as dry or boring. To me, it has always been a way to make sense of the world. Over the years, I realized how deeply math is woven into nature, music, technology, and even philosophy.

Now, I’m pursuing a Master’s degree in Mathematics to deepen my foundations and challenge myself intellectually. From calculus to graph theory, each subject opens up a new way of thinking. I enjoy proofs as much as applications, and I love how abstract concepts often find real-world meaning in AI algorithms or data structures.

📘 Mathematical Foundations for AI & Data Science

My background in backend development and data science led me to explore machine learning, where I discovered just how vital math truly is. Linear algebra powers neural networks, probability drives decision trees, and optimization lies at the heart of deep learning. I don’t just want to use models — I want to understand and improve them.

As I study M.Sc. Mathematics, I often connect academic concepts with my AI projects — blending theory with practical insight. This mathematical grounding gives me the confidence to build better, smarter systems. I also see strong connections between math and my other interests — like astrological computations and planetary modeling.

Mathematics, for me, is more than a tool — it’s a lifelong pursuit of beauty, truth, and structure. And I’m just getting started.

🧮 Topics I Deeply Enjoy

🔢 Linear Algebra

Linear Algebra forms the backbone of modern machine learning. I’m fascinated by how vectors, matrices, eigenvalues, and transformations bring structure to data and help build powerful algorithms like PCA, neural networks, and recommendation systems.

🧱 Matrices & Determinants

I enjoy working with matrices and determinants — especially their role in solving linear systems, calculating transformations, and performing operations in image processing, 3D graphics, and network flows.

📊 Probability & Statistics

Probability helps me model uncertainty and randomness, which is essential in AI. I’m passionate about statistical thinking, distributions, hypothesis testing, and Bayesian inference — especially their applications in predictive modeling.

🌐 Calculus

Graph Theory has a magical appeal to me — it’s amazing how graphs model relationships, networks, social structures, and even knowledge representation in AI. I’m exploring shortest paths, connectivity, and graph-based algorithms in detail.

🧮 Number Theory

Though more abstract, I find number theory intellectually rewarding. It shows up in cryptography and pattern recognition. I often explore topics like prime numbers, divisibility, and modular arithmetic as a mental workout.