visualmath
r/VisualMath - Gradient descent at the very core of Artificial Intelligence
In the end training a network is the solution of a very high dimensional non-linear optimization problem ( finding the minimum of a function). The graphic shows a two dimensional optimization problem and how the gradient descent algorithm aproaches the minimum ( center). In 2D this is trivial, in higher dimensions computationally intensive. The slider sets the step size. You want big steps to find the solution fast, but if the step size gets to big, the optimizer starts to oscilate.