Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties (e...

of gradient descent, stochastic gradient descent, serves as the most basic algorithm used for training most deep networks today. Gradient descent is based...

out-of-core versions of machine learning algorithms, for example, stochastic gradient descent. When combined with backpropagation, this is currently the de...

of stochastic gradient descent, where gradients are computed on a random subset of the total dataset and then used to make one step of the gradient descent...

Stochastic gradient Langevin dynamics (SGLD) is an optimization and sampling technique composed of characteristics from Stochastic gradient descent, a...

Gradient descent Stochastic gradient descent Wolfe conditions Absil, P. A.; Mahony, R.; Andrews, B. (2005). "Convergence of the iterates of Descent methods...

can be derived through dynamic programming. Gradient descent, or variants such as stochastic gradient descent, are commonly used. Strictly the term backpropagation...

for all nodes in the tree. Typically, stochastic gradient descent (SGD) is used to train the network. The gradient is computed using backpropagation through...

being stuck at local minima. One can also apply a widespread stochastic gradient descent method with iterative projection to solve this problem. The idea...

descent Stochastic gradient descent Coordinate descent Frank–Wolfe algorithm Landweber iteration Random coordinate descent Conjugate gradient method Derivation...