<human>:  When I run the deep learning algorithm called Coordinate Descent, it seems to converge to a local minimum. What does that mean?
<bot>: A local minimum is a point in the function space where the gradient is zero. It’s a point where the function is not changing much. In deep learning, the gradient is the rate of change of the loss function with respect to the parameters. If the gradient is zero, then the parameters are not changing much, and the loss function is not changing much either. This means that the algorithm is converging to a local minimum.