Optimizer, Gradient Descent, Machine Learning, Adam, Sgd.
minima it takes very small steps and moves very carefully, so there is very little chance of overstepping the optimum point. It also helps to avoid saddle points and the local minima. So, it uses a modified learning rate parameter that adapts to the changes over time. So, it changes the learning