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How Does Backpropagation in a Neural Network Work? | Built In

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Backpropagation is the neural network training process of feeding error rates back through a neural network to make it more accurate. Here’s what you need to know.

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A neural network learns by continuously adjusting its parameters - the weights and biases - to improve its prediction accuracy. These parameters are tweaked upward or downward based on how far the model’s predictions deviate from the actual results. The difference between the predicted output and the true value is measured using a loss function, which quantifies the error. To minimize this error, the model uses backpropagation, an algorithm that calculates the gradients (partial derivatives of t

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A neural network learns by continuously adjusting its parameters - the weights

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