Proper initialization of neural network weights is important primarily to ensure faster and more stable convergence during training. It helps avoid problems such as vanishing or exploding gradients by setting the initial values so that signals propagate well forward and gradients propagate well backward through the network. Poor initialization, such as all weights being zero or too large, can lead to slow learning, inability to break symmetry among neurons, or unstable training that fails to converge properly. This initial setting of weights thus directly influences how effectively and quickly the model learns from data. Initialization does not directly prevent overfitting, increase model capacity, or minimize the number of layers, but it plays a crucial role in starting the optimization on the right footing for effective training.
