Neural networks are "inspired by the brain" the way airplanes are "inspired by birds."

They share the fundamental idea (learning from data / generating lift) but the implementation is completely different. And understanding WHY they work requires math, not biology.

If you want to genuinely understand deep learning, start with the math: linear algebra (matrix operations are the backbone), calculus (backpropagation is just the chain rule), and probability (loss functions are expectations).

You don't need to be a mathematician. But you need enough math literacy to understand what your code is actually doing.

The engineers who treat deep learning as a black box eventually hit a wall. The ones who understand the math? They can debug anything, design novel architectures, and know when deep learning is the wrong tool entirely.

Invest a month in the math foundations. It pays dividends for your entire career.