Welcome to today’s quick deep dive into training neural networks! You’ve probably heard how easy it is to get started—just a few lines of code, and you’re off to the races. But here’s the catch: neural net training is a *leaky abstraction*. Unlike simple APIs, understanding what's happening under the hood is crucial. For example, libraries hide the complexity of backpropagation, batch normalization, or RNNs, but if you don’t grasp how they work, you risk silent failures that can derail your project.

And speaking of failures, neural net training often fails silently. You might not get obvious errors—no exceptions popping up—yet your model could be misconfigured. Maybe your labels got flipped, or your data augmentation is inconsistent. The network might still seem to work because it learns some quirks, but that’s not reliable.

The key? Develop a process to troubleshoot and understand your models deeply. Don’t just rely on plug-and-play solutions. Dive in, learn the mechanics, and keep a keen eye on your training pipeline. That’s the recipe for truly mastering neural networks!