How do you gang stack for GD?

Answer 1

Gang stacking for gradient descent (GD) involves grouping multiple training examples together to optimize the model's parameters more efficiently. This technique can help in reducing the variance of the gradient estimates by averaging the gradients over a batch of examples, leading to more stable updates. Typically, you would select a number of training examples, compute the gradients for the entire batch, and then update the model parameters based on the average gradient of the batch. This approach is often referred to as mini-batch gradient descent.