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The Delta rule in machine learning and neural network environments is a specific type of backpropagation that helps to refine connectionist ML/AI networks, making connections between inputs and outputs with layers of artificial neurons.
The Delta rule is also known as the Delta learning rule.
Techopedia Explains Delta Rule
In general, backpropagation has to do with recalculating input weights for artificial neurons using a gradient method. Delta learning does this using the difference between a target activation and an actual obtained activation. Using a linear activation function, network connections are adjusted.
Another way to explain the Delta rule is that it uses an error function to perform gradient descent learning.
A tutorial on the Delta rule explains that essentially in comparing an actual output with a targeted output, the technology tries to find a match. If there is not a match, the program makes changes. The actual implementation of the Delta rule is going to vary according to the network and its composition, but by employing a linear activation function, the Delta rule can be useful in refining some types of neural network systems with particular flavors of backpropagation.
