Guide to Gradient Descent Optimization Algorithms Used in Machine Learning

Gradient descent is a widely used first-order iterative optimization algorithm that minimizes a differentiable function by repeatedly moving in the direction opposite to its gradient. It plays a central role in training machine learning and AI models by reducing cost or loss functions. Three main variants exist — batch, stochastic, and mini-batch gradient descent — each differing in how much training data is used per parameter update. Batch gradient descent computes gradients over the entire dataset before updating parameters, guaranteeing convergence but proving slow for large datasets. Stochastic gradient descent, by contrast, updates parameters for each individual training example, making it faster and suitable for online learning but prone to high variance in the objective function.