How Switching Mental Models Made Neural Networks Finally Click

A developer writing on DEV Community describes how a purely mechanical understanding of neural networks — tracking data through layers and weight updates — left the bigger picture unclear. Watching Josh Starmer's StatQuest explainer introduced the idea of neural networks as systems that sculpt functions, providing a useful zoomed-out perspective. The author then explored multiple mental models, including stack-of-transformations and function-approximation views, finding that each one made different questions easier to answer. Framing neural networks as function approximators connected the technology to familiar models like linear regression, reducing much of its perceived mystery. The key insight was that fluency came not from any single model but from learning to switch between several depending on the question at hand.