A gradient step refers to the process of updating the parameters of a machine learning model based on the computed gradient of a loss function with respect to those parameters. This process is a fundamental part of optimization algorithms used in training models, particularly in methods like gradient descent.
In the context of machine learning, the goal is to minimize a loss function, which quantifies how well the model’s predictions match the actual data. During each iteration of the training process, the gradient of the loss function is calculated. This gradient indicates the direction and rate of steepest ascent of the loss function. By taking a step in the opposite direction of the gradient, the parameters are adjusted to reduce the loss.
The size of the step taken during this update is determined by the learning rate, a hyperparameter that controls how much to change the model in response to the estimated error each time the model weights are updated. A smaller learning rate may lead to more precise convergence but can slow down the training process, while a larger learning rate may speed up training but risks overshooting the optimal parameters.
In summary, a gradient step is a critical element in the iterative process of training machine learning models, facilitating the adjustment of parameters to improve the model’s accuracy over time.