Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. The learning rate controls how quickly the model is adapted to the problem. What does lowering learning rate in gradient descent lead to?
Admin Staff asked 4 years ago
Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. The learning rate controls how quickly the model is adapted to the problem. What does lowering learning rate in gradient descent lead to?
a.Gradient overshooting
b.Slower convergence time
c.fewer iterations for convergence
d.High accuracy