Gradient Descent
An optimization algorithm that iteratively updates model parameters to minimize prediction error and improve model performance.
What is Gradient Descent?
Gradient descent calculates the direction in which a model’s loss function increases most rapidly and then updates the model’s parameters in the opposite direction. The size of each update is controlled by the learning rate. This process is repeated over multiple iterations until the model reaches a point where the loss is minimized or stops improving significantly.
Why is Gradient Descent Important?
Training many machine learning models involves finding parameter values that produce the smallest possible error. Gradient descent provides an efficient way to search for these values, even when models contain millions or billions of parameters. It forms the basis of many optimization techniques used in modern deep learning.
Common use cases
Gradient descent is commonly used in neural networks, deep learning, linear regression, logistic regression, computer vision, and large language model training.