You trained a model. The loss went down. Validation accuracy looked fine. You deployed it, and now it's producing garbage on real data. Sound familiar? I've been there more times than I'd like to admit. And here's the uncomfortable truth that a recent academic discussion on the theoretical foundations of deep learning reinforces: we still don't have a complete scientific theory for why deep learning works. We have intuitions, heuristics, and a lot of empirical results — but when your model breaks in production, that gap in understanding hits hard. Let me walk you through the debugging process I've developed after years of shipping models that occasionally decided to embarrass me. The Core Problem: Black Box Debugging Deep learning sits in a weird spot in software engineering. With traditional code, you can trace execution, inspect state, and reason about behavior deterministically. With neural networks, you're dealing with millions of parameters that interact in ways nobody fully understands.…