Random Initialization in machine learning refers to the process of setting the initial values of the weights (parameters) of a neural network randomly. This is a critical step to break symmetry and ensure that the model learns various features during training.
The Role of Random Initialization
Without random initialization, all neurons in a given layer of a neural network would learn the same features during training, rendering additional neurons redundant. Random initialization ensures that neurons start off in different states, allowing for diverse feature learning and more efficient training.
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