gen-state-gradient-descent-precisely-and-explain-why-it-matters-028d

State gradient descent precisely and explain why it matters in 2-3 sentences.

# Gradient Descent **Precise statement:** Gradient descent is an iterative optimization algorithm that finds a local minimum of a differentiable function f(x) by repeatedly updating the parameters in the direction opposite to the gradient: **x_{t+1} = x_t - η∇f(x_t)**, where η is the learning rate. **Why it matters:** Gradient descent is the fundamental algorithm behind training neural networks and most modern machine learning models, enabling them to learn from data by minimizing loss functions. Without an efficient method to optimize functions with millions or billions of parameters, deep learning and AI as we know it would be computationally infeasible.