Gradient descent is a popular algorithm for optimizing neural networks, and various optimization algorithms have been developed to improve its performance, including stochastic gradient descent, mini-batch gradient descent, and adaptive learning rate methods such as Adagrad, Adadelta, RMSprop, and Adam. These algorithms aim to address challenges such as slow convergence, oscillations, and ...