Xavier's initialization

Xavier's initialization, also known as Glorot initialization, is a method used to set the initial weights of artificial neural networks to help with the convergence of training. It sets the weights to values drawn from a distribution with zero mean and a variance that depends on the number of input and output neurons, aiming to maintain the variance of the outputs of each neuron close to 1 during the beginning of training.