1. Introduction “If you tell me how you reward me, I’ll tell you how I’ll behave.” – This applies to both humans and reinforcement learning agents. When I first started working with RL models, I assumed the reward function was just a simple scoring mechanism—higher rewards mean better learning, right? Wrong. A poorly designed reward … Read more
1. Introduction “All models are wrong, but some are useful.” – George Box K-Means is one of those algorithms that has stood the test of time. Despite being one of the simplest clustering techniques, I still find myself coming back to it for a surprising number of projects. But here’s the catch—K-Means is both overused … Read more
1. Introduction Why Normalization Matters in Deep Learning? I’ve spent a lot of time training deep learning models, and one thing I’ve learned the hard way is that training instability is a silent killer. You tweak hyperparameters, try different architectures, and still, the loss graph looks like a stock market crash. Turns out, a lot … Read more
Introduction: Why Clustering Matters More Than You Think “If you torture the data long enough, it will confess to anything.” — Ronald Coase Clustering is one of those techniques that sounds simple—group similar things together—but once you actually start using it, you realize it’s a whole different beast. I remember the first time I had … Read more
1. Introduction “The brain isn’t just a processor; it’s a liquid network of ever-changing patterns.” – This idea, deeply rooted in neuroscience, is what makes Liquid State Machines (LSMs) so fascinating. I remember when I first came across Liquid State Machines—I was working on a project that required real-time spatiotemporal pattern recognition (think speech recognition … Read more
1. Introduction “A model is only as good as its hyperparameters.” When I first started working with Decision Trees, I made a classic mistake: I thought the default parameters were “good enough.” Sure, they worked, but the results were nowhere near optimal. Sometimes the model overfit like crazy, capturing every little detail of the training … Read more
1. Introduction “If I have seen further, it is by standing on the shoulders of giants.” – Isaac Newton. I’ve always believed that understanding how search engines work is a superpower in the digital world. And if there’s one algorithm that laid the foundation for modern search, it’s PageRank—the brainchild of Larry Page and Sergey … Read more
1. Introduction: Why This Comparison Matters “In machine learning, picking the right model isn’t about what’s ‘better’—it’s about what’s right for your data.” I’ve seen it happen countless times—someone jumps straight into Linear Regression because it’s familiar, or they throw Random Forest at a problem because they heard it’s powerful. But without understanding how these … Read more
1. Introduction “If words are bricks, is meaning just a stack of bricks? Not quite. That’s the key difference between word and sentence embeddings.” I’ve spent years working with NLP models, and if there’s one thing I’ve learned, it’s this: context is everything. A word alone tells you very little—how it’s used in a sentence … Read more
