Xavier or Glorot initialization is a technique used to set the initial weights of neural networks, aiming to maintain a balanced variance of activations throughout the layers. This method helps mitigate issues like vanishing and exploding gradients, which can significantly hinder the training process in deep networks. By scaling the weights according to the number of input and output units, it ensures that the gradients during backpropagation do not diminish to zero or blow up to infinity, thus facilitating effective learning.
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