What is the benefit of weight initialisation in neural networks?
The weights are initialized in neural networks to surpass the above problems. The gradient can vanish or explode rapidly if weights are not initialised during the forward pass through the deep neural network. That can cause the slow convergence of the network, or they may not even converge in some cases. It also ensures that we will not oscillate near the minima.
