Hi everyone! I’m thrilled to join this community. I am currently deep-diving into Applied Mathematics, specifically focusing on the mathematical foundations of AI, like Linear Algebra (matrices, determinants) and Calculus. While I love the theoretical side, my goal here is to bridge the gap between abstract math and practical AI Automation. I want to see how the equations I solve on paper turn into efficient workflows and intelligent systems. I’m looking forward to learning from your experiences and sharing my journey as I apply mathematical logic to real-world data projects. If anyone has tips on the best path from 'math-heavy theory' to 'hands-on AI building,' I’d love to connect! Happy to be here!

Hi everyone! As someone passionate about Applied Mathematics and AI Research, I’ve noticed that while many people use AI models, the underlying mathematics - like Eigenanalysis - often remains a "black box." I recently published a deep dive on Medium titled "Eigenanalysis" to bridge this gap. In the article, I break down: - The core intuition behind Eigenvalues and Eigenvectors. - How this theory powers Principal Component Analysis (PCA). - Real-world applications in Computer Vision (like Eigenfaces). If you’re interested in understanding the "why" behind the algorithms we use every day, check it out here: https://medium.com/@nihatgariblii/eigenanalysis-from-intuition-to-practical-python-6c0daf234957 I’d love to hear your thoughts or answer any questions you have about the math behind AI!

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