Thoughts, insights, and experiences from my journey in technology and research.
Beyond simple addition or multiplication, advanced matrix operations like LU decomposition, Moore-Penrose pseudoinverse, and others unlock powerful ways to solve complex systems of equations, invert non-square matrices, and optimize algorithms efficiently. In this blog, we’ll break down these essential techniques, explore their mathematical foundations, and show how they’re used in real-world applications—from numerical analysis to deep learning.
Matrix algebra forms the bedrock of many modern applications in computer science, data science, physics, and engineering. From transforming images to training machine learning models, matrices are everywhere—quietly powering the math behind the scenes. In this blog, we’ll break down the basics of matrix algebra. Whether you're just getting started or brushing up, this guide will help you build a strong foundation in understanding how matrices work and why they're so essential.
Neural networks have revolutionized how machines learn from data—powering everything from voice assistants to medical diagnosis tools. But beneath the buzzwords and real-world applications lies a fascinating world of math that makes it all work. In this blog, we’ll dive into the mathematical foundations of neural networks—covering concepts like weighted sums, activation functions, backpropagation, and gradient descent—while also exploring how these powerful models are used across domains like computer vision, natural language processing, healthcare, and beyond.
This project tackles plant disease detection using a multi-class classification approach with CNNs, leveraging EfficientNet for its high accuracy and efficiency on limited hardware. To further optimize performance, a two-stage pipeline was introduced: first identifying the plant species, then classifying the disease using species-specific models. This hierarchical method not only reduced computational load but also improved accuracy by narrowing the classification scope in the second stage.
Growth often feels invisible—until it isn’t. Inspired by the quiet power of bamboo, this blog explores how foundational work in learning and problem-solving, especially in machine learning and AI, mirrors nature’s most patient plant. This post dives into how embracing the “root-growing” phase can transform how we approach challenges, projects, and growth itself.