Gradient descent algorithms take the loss function and use partial derivatives to determine what each variable (weights and biases) in the network contributed to the loss value. It then moves ...

However, the gradient descent algorithms need to update variables one by one to calculate the loss function with each iteration, which leads to a large amount of computation and a long training time.