Gradient-based methods

Gradient-based methods are techniques used in optimization to find the best solution by gradually adjusting parameters to minimize or maximize a function. Think of it like hiking down a hill to reach the lowest point; the gradient indicates the steepness or slope, guiding each step in the direction that leads closer to optimality. These methods calculate the gradient (rate of change) at each point and use it to iteratively update the parameters. Common examples include gradient descent, widely used in training machine learning models to improve accuracy efficiently.