juniorCost Function
What is gradient descent in cost optimization?
Updated May 15, 2026
Short answer
Gradient descent iteratively reduces cost by updating parameters in the opposite direction of the gradient.
Deep explanation
It computes partial derivatives of the cost function and updates parameters step by step to reach a minimum.
Real-world example
Used in training neural networks for image recognition.
Common mistakes
- Using too high learning rate causing divergence.
Follow-up questions
- What is learning rate?
- What are local minima?