Deep Learning

Overfitting. The bane of every machine learning engineer’s existence. You train your model, it performs amazingly on training data, and then… it completely fails on real-world data. Sound familiar? I remember the first time I encountered this problem. I was working on an image classification task, my model was getting 99% accuracy on training data but only 96% on validation. At first glance, it seemed fine - but that 3% gap was a red flag. The model was starting to memorize specific training examples instead of learning generalizable patterns. ...

What’s SGD All About? Picture this: you’re trying to teach a computer to recognize cat pics or recommend your next binge-worthy show. To do that, you need to tweak a bunch of numbers (aka model parameters) to make your predictions as spot-on as possible. That’s where Stochastic Gradient Descent (SGD) swoops in like a superhero. It’s a nifty algorithm that helps machine learning models learn by nudging those numbers in the right direction, bit by bit. Think of it as finding the lowest point in a foggy valley by taking small, semi-random steps. Let’s break it down, human-style! ...