In mathematics, gradientdescent (also often called steepest descent) is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent.
Gradient descent in machine learning; Incremental gradient descent; AdaGrad; Adagrad; Adam (optimization algorithm); Momentum (machine learning); RMSProp; Applications of stochastic gradient descent; Stochastic Gradient Descent; SGD optimizer; Adam optimizer; RMSprop
Stochastic gradientdescent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g.