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We update the weights and biases by subtracting the product of the learning rate and the gradient from the current values. The *learning rate* is a hyperparameter that controls the step size of the update. A smaller learning rate will result in slower but more stable learning, while a larger learning rate will result in faster but potentially less stable learning.
