#010 C Random initialization of parameters in a Neural Network - Master Data Science

Why do we need a random initialization? In other words unit1 and unit2 are symmetric, and it can be shown by induction that these two units are computing the same function after every iteration of training. Even if we have a lot of hidden units in the hidden layer they all are symetric if we initialize corresponding parameters to zeros. To solve this problem we need to initialize randomly rather then with zeros. And then we can initialize \(b_1\) with zeros, because initialization of \(W_1\) breaks the symmetry, and unit1 and unit2 will not output the same value even if we initialize \(b_1\) to zero.