Backpropagation is essential in neural networks because it enables the fine-tuning of weights based on the error rate from predictions, thus improving accuracy. This algorithm efficiently calculates how much each weight contributes to overall error by applying the chain rule, allowing the network to minimize its loss function through iterative updates [5][6][12]. Its effectiveness in training deep networks has led to its widespread adoption in various machine learning applications [7][11].

Why is "Backpropagation" essential in neural networks?

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