What is the delta rule method?

In the context of a single-layer neural network, the delta rule provides a mechanism for updating the weights associated with the inputs to each neuron. This process is crucial for enabling the network to learn and adapt to new data.

The rule works by calculating the error between the network's output and the desired output for a given set of inputs. Based on this error, the weights are adjusted in a direction that minimizes the error for future inputs.

Machine learning, a field that has revolutionized various industries, relies heavily on algorithms to enhance the capabilities of machines. Among these algorithms, the delta rule stands out as a fundamental principle in training neural networks.

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