👉 Updating math, often referred to as differential updates or gradient descent with momentum, is a method used to iteratively adjust parameters in machine learning models to minimize a loss function. The core idea is to compute the gradient of the loss function with respect to the parameters, then update the parameters in the direction that reduces this gradient. The update rule typically involves subtracting a fraction of the previous update (to incorporate momentum) and adding the new gradient scaled by a learning rate. This process is repeated for each data point (or mini-batch), gradually refining the model's parameters to better fit the training data. Advanced variants like Adam and RMSprop further optimize this process by adapting the learning rate for each parameter based on the historical gradients.
