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! 👋 Dealing with high-dimensional data is one of the biggest challenges in Data Science. It’s hard to visualize, computationally expensive, and often full of noise. I’ve been working with Principal Component Analysis (PCA) lately to streamline this process. It’s incredible how we can reduce the number of variables while still capturing the essence of the dataset. In my latest project, I covered: - Data Preparation: Getting the features ready for reduction. - PCA Implementation: Using Python to find the most important components. - Visualization: Turning abstract data into clear, interpretable plots. I’m curious, what’s your favorite technique for dimensionality reduction? Do you stick with PCA, or do you prefer T-SNE / UMAP for your projects? I’ve documented my entire workflow and code in a new article. I’ll leave the Medium link in the comments for anyone who wants to check it out! 👇 #DataScience #MachineLearning #Python #AI #DataAnalysis

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!

Hey all, I want to gather some feedback from people who are either starting their freelancing journey on Upwork or are seasoned vets. When you provide answers to these questions please include your experience level on Upwork (number of jobs, time on platform) 1. What is the number one confusion point for you with how to write better proposals? 2. Do you ever think about how to optimize your profile? 3. Do you feel you have a strong grasp on going from initial message to closed deal? 4. Do you focus on more proposals or better proposals 5. Do you boost every time? Only sometimes? Would love to hear from yall @Ryan Nolan