Gradient Descent
Domain: AI Fundamentals
🎯 Motivation
Adjusts weights step by step downhill on the loss surface to reduce error.
📋 Concrete Example
If the loss goes down when you increase weight A, gradient descent increases weight A a little. Repeat.
🔗 Analogy
Like hiking down a foggy mountain by always stepping in the steepest downward direction.