He initialization is a method used to set the initial weights of neural network layers, particularly effective for networks using ReLU activation functions. This technique helps mitigate problems like vanishing and exploding gradients by scaling the weights based on the number of input neurons. Proper weight initialization is crucial in training deep networks, as it influences convergence speed and overall model performance.
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