Gradient Descent Variants

Gradient descent variants are algorithms used to optimize models by iteratively adjusting parameters to minimize errors. The basic version updates parameters using the full dataset (batch gradient descent), which can be slow for large data. Stochastic gradient descent (SGD) updates parameters using one data point at a time, making it faster and more scalable but noisier. Mini-batch gradient descent strikes a balance by updating with small groups of data points, improving efficiency and stability. Variants like momentum or Adam include techniques to accelerate convergence and avoid getting stuck in suboptimal solutions, enhancing overall training performance.