What is gradient descent convergence?
Updated May 16, 2026
Short answer
Convergence occurs when gradient descent reaches a stable minimum of the cost function.
Deep explanation
At convergence, parameter updates become negligible because gradients approach zero. Proper learning rate ensures stable convergence.
Real-world example
Training predictive pricing models until loss stabilizes.
Common mistakes
- Assuming convergence guarantees global minimum.
Follow-up questions
- Can gradient descent get stuck?
- What improves convergence speed?