How does Learning Rate affect Gradients in Back Prop
The gradient descent algorithm has the learning rate term in it. Since the gradient descent is backpropagated, the learning rate is taken into account during backpropagation.
I was wondering if the learning rate is set to update the weights during the backpropagation on the layers of a neural network. How and where do the weights get updated?
I can't see the connection between optimizer, learning rate, and the backpropagation function.