I hear a lot about local minima for neural networks. I understand the theory behind it - but if my neural network finds weights in a local minimum, is that a bad thing?
I understand that finding global minima (in Neural Networks) is usually a bad thing as well, since global minima usually overfits. However I am still a bit confused.
Do convergence to local minima give us bad solutions? Lastly, does convergence to local minima overfit to our training data?