Gradient-based Optimization

Gradient-based optimization is a method used in machine learning and artificial intelligence to update the parameters of a model to minimize a loss function. The loss function measures the error between the model’s predictions and the actual outputs and is used in the optimization process.

In gradient-based optimization, the gradient of the loss function with respect to the model parameters is computed using automatic differentiation. The gradient provides information about how the model parameters should be updated to reduce the loss.