How does cost function scaling affect numerical stability in deep learning?

Improper scaling can cause gradient explosion or vanishing issues.

Cost functions with large magnitude outputs can lead to unstable gradients during backpropagation. Similarly, very small values can cause underflow and slow learning. Proper normalization, loss scaling, and mixed precision training are used to stabilize computation.