Can you explain what bias terms are and why we need them in a neural networK?
motoole2
Earlier in this lecture, we introduced the perceptron, which worked by fitting a hyperplane through some N-dimensional space and labelling all points on one side +1 and all points on the other -1. The weights determined the orientation of the hyperplane. However, because this perceptron didn't have a bias, the hyperplane always had to pass through the origin.
The bias term provides a way to translate this hyperplane, which is a helpful property for perceptrons to make decisions (similar to when we discussed support vector machines at the end of lecture 14).
Can you explain what bias terms are and why we need them in a neural networK?
Earlier in this lecture, we introduced the perceptron, which worked by fitting a hyperplane through some N-dimensional space and labelling all points on one side +1 and all points on the other -1. The weights determined the orientation of the hyperplane. However, because this perceptron didn't have a bias, the hyperplane always had to pass through the origin.
The bias term provides a way to translate this hyperplane, which is a helpful property for perceptrons to make decisions (similar to when we discussed support vector machines at the end of lecture 14).