Draw Delta Learning Rule (LMS-Widrow Hoff) model and explain it with a training process flowchart.

The Delta Learning Rule, also known as the LMS (Least Mean Squares) algorithm or the Widrow-Hoff rule, is a method for adjusting the weights of a linear neuron in a neural network during training. Below is a graphical representation of the Delta Learning Rule model:

Components of the Delta Learning Rule Model:

1. Input Features:
   • Represents the input data features (e.g., pixel values in an image, numerical attributes in a dataset).
2. Weighted Summation:
   • Each input feature is multiplied by its corresponding weight.
   • The weighted inputs are summed up to produce a weighted sum.
3. Activation Function:
   • The weighted sum is passed through an activation function.
   • Common activation functions include the step function, sigmoid function, or rectified linear unit (ReLU).
4. Output:
   • Represents the output of the neuron after applying the activation function.
   • The output can be binary (0 or 1) for binary classification tasks or continuous for regression tasks.

Operation of the Delta Learning Rule:

1. Initialization:
   • Initialize the weights of the neuron with random or predefined values.
2. Forward Propagation:
   • Pass the input features through the neuron.
   • Calculate the weighted sum of inputs.
   • Apply the activation function to obtain the output.
3. Error Calculation:
   • Compare the predicted output to the true output (target).
   • Calculate the error as the difference between the predicted output and the target.
4. Weight Adjustment (Learning):
   • Update the weights based on the error using the Delta Rule formula: Δwij​=η⋅(di​−yi​)⋅xij​ Where:
     •  Δwij​ is the change in weight.
     • η is the learning rate.
     • di​ is the target output.
     • yi​ is the predicted output.
     • xij​ is the input feature.
   • Adjust the weights to minimize the error and improve the accuracy of the model.
5. Repeat:
   • Repeat steps 2-4 for each training example in the dataset.
   • Iterate through the entire dataset multiple times (epochs) until the model converges or until a stopping criterion is met.