Gradient Descent Algorithms

Gradient descent is an optimization method used to find the best solution by minimizing errors or costs. Imagine you're at the top of a hill and want to reach the lowest valley nearby. You take steps downhill, adjusting your direction based on the slope at each point. In algorithms, this means repeatedly updating parameters by moving opposite the gradient — the steepest ascent — to reduce loss. This process continues until reaching a point where further steps don't significantly improve performance, ideally finding the optimal solution efficiently.