In the context of machine learning, a weight is a parameter within a neural network that transforms input data within the network's layers. Weights are adjusted during the training process to minimize the loss function.
How Weights Influence Learning
Weights in a neural network determine the importance of input features and how they impact the network's output. The process of learning involves updating these weights based on the gradient of the error with respect to each weight, typically using backpropagation.
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