Bigger isn’t always better. Sometimes, simple wins.
There’s a saying in programming: “If it works, it’s not stupid.”
I learned this the hard way when a simple logistic regression model outperformed my meticulously crafted deep learning architecture. Let me share that story with you because, honestly, it taught me more about ML than any course or textbook ever did.
The Backstory: When Complexity Became My Comfort Zone
As someone who’s obsessed with automation, I’ve spent years building scripts and models that save time, streamline workflows, and make life easier. So naturally, when a dataset landed on my desk (or rather, my Downloads folder), my first instinct was:
Let’s build something serious.
I fired up my Jupyter Notebook, imported TensorFlow, and started designing a neural network that could make Yann LeCun proud. Layers? Check. Dropout? Check. Batch normalization? Check.
Fast-forward three days later, my model looked like a skyscraper. It had so many layers, it felt like an ego boost in code form. But here’s the catch:
It performed like garbage.