Gradient Machine Learning
noun
Definition 1: A vector of partial derivatives indicating the direction and rate of steepest increase of a function [Britannica].
Example in context: “Therefore, in order to effectively classify the no artificial labeling original data with mixed temporal correlation features, we construct the composite binary relation and its rough set model based on the gradient and cosine similarity.” [Xiaoli et al. 2024]
Definition 2: In machine learning, a gradient is used to update model parameters during training, typically in optimization procedures aimed at minimizing a loss function [Google Machine Learning Crash Course].
“Another study Prakash and Avestimehr (2020) showed that even when each client utilized a mini-batch for computing the gradient, a noisy media technique improved the convergence performance with non-IID data while training with small neural networks and the Modified National Institute of Standards and Technology dataset.” [Zhang et al. 2024]
Related terms: partial derivatives, loss gradient, backpropagated gradient