1. Why OpenAI + MyScale Is a Killer Stack for Recommendations “Simple systems scale better. Smart systems personalize better. The sweet spot? You want both.” Over the past few months, I’ve built and tested a handful of recommendation systems using everything from pure Faiss + Flask setups to vector support in PostgreSQL, and even a … Read more
1. Introduction: Why Embeddings Are a Cornerstone of Modern ML Pipelines “If you can’t measure meaning, you can’t optimize for it.” That’s been one of the most important lessons I’ve learned building and scaling ML pipelines over the years. And when it comes to encoding “meaning” into numbers, nothing beats vector embeddings. I’m not going … Read more
1. Introduction “The model’s only as smart as the data you feed it.” I’ve seen this play out over and over — teams pouring months into model tuning, only to get mediocre results because the dataset was a mess. The real bottleneck? It’s almost never the model architecture. It’s always the data — its structure, … Read more
I. Introduction “If you do what you’ve always done, you’ll get what you’ve always gotten.” – Tony Robbins. When I first started working with convolutional neural networks (CNNs) for image classification, I was blown away by their ability to detect edges, textures, and patterns. But as I moved on to more complex datasets—think medical imaging … Read more
Introduction When I first started exploring Reinforcement Learning (RL), I quickly realized it wasn’t just another buzzword — it was transforming the way AI behaves in games. Unlike traditional rule-based systems, RL agents don’t rely on pre-programmed strategies; instead, they learn through experience, much like how we refine our skills by trial and error. If … Read more
1. Introduction Motivation & Relevance You ever find yourself staring at a massive dataset, wondering, How do I make sense of this mess? Yeah, I’ve been there too. When I first started working with clustering algorithms, K-Means quickly became my go-to tool. It’s simple yet surprisingly powerful—one of those rare algorithms that just works in … Read more
1. Introduction: Why Linear Regression for House Prices? When I first started working on house price prediction, I assumed that complex models like XGBoost or deep learning would always outperform traditional methods. But experience has taught me that sometimes, simpler is better—and that’s exactly where linear regression shines. Linear regression is a workhorse in real … Read more
1️⃣ Introduction “The only way to learn mathematics is to do mathematics.” — Paul Halmos If you’ve ever worked with machine learning, you already know this: linear regression is everywhere. From predicting house prices to understanding marketing trends, it’s often the first model we reach for. But here’s something most tutorials won’t tell you—knowing the … Read more
1. Introduction “Clustering isn’t just an algorithm—it’s a way of seeing patterns the human eye would never catch.” When I first got into clustering, I thought, “How hard can it be? Just group similar things together.” But once you start working with real-world data, you quickly realize that clustering is both an art and a … Read more
Introduction If you’re here, chances are you already love deep learning and want to build serious projects with TensorFlow. I get it—I’ve been there myself, experimenting, failing, optimizing, and eventually deploying real-world models. Over the years, TensorFlow has become my go-to deep learning framework for one reason: it’s not just about building models—it’s about taking … Read more
