I'm not an AI expert either, but let me give this a try.
I assume you are vaguely familiar with gradient descent. In gradient descent, we are basically trying to find the sweet spot where the value of a function is minimized. We do this by calculating the derivative of the function at a certain point and then use it to take small steps in the direction where we believe the function will have a lower value.
Gradient descent usually suffers from a problem where the algorithm gets stuck in local minimas if the function is not convex in shape.
However, when people use gradient descent to optimize functions with a very large number of parameters (as is the case in Deep Learning), another problem surfaces called saddle points. Imagine a 3 dimensional plot of the function at different values of its parameters (in reality the plot will be multi-dimensional). Now on this plot, there will be many regions where the derivative of the components defining the surface become zero. This messes with our plan to use derivatives to find the direction in which to move. So we need to come up with strategies to escape saddle points during the gradient descent process.
I assume you are vaguely familiar with gradient descent. In gradient descent, we are basically trying to find the sweet spot where the value of a function is minimized. We do this by calculating the derivative of the function at a certain point and then use it to take small steps in the direction where we believe the function will have a lower value.
Gradient descent usually suffers from a problem where the algorithm gets stuck in local minimas if the function is not convex in shape.
However, when people use gradient descent to optimize functions with a very large number of parameters (as is the case in Deep Learning), another problem surfaces called saddle points. Imagine a 3 dimensional plot of the function at different values of its parameters (in reality the plot will be multi-dimensional). Now on this plot, there will be many regions where the derivative of the components defining the surface become zero. This messes with our plan to use derivatives to find the direction in which to move. So we need to come up with strategies to escape saddle points during the gradient descent process.