What is the weight update rule in backpropagation?Select one:a.W(i, j) = W(i, j) - alpha * delta(i) * output(j)b.W(i, j) = W(i, j) + alpha * delta(i) * output(j)c.W(i, j) = W(i, j) / alpha * delta(i) * output(j)d.W(i, j) = W(i, j) * alpha * delta(i) * output(j)

What is the bias update rule in backpropagation?Select one:a.B(i) = B(i) / alpha * delta(i)b.B(i) = B(i) - alpha * delta(i)c.B(i) = B(i) * alpha * delta(i)d.B(i) = B(i) + alpha * delta(i)

What is the bias update rule in the backpropagation algorithm?Select one:a.The process of minimizing the error between the predicted output and the actual outputb.The process of adjusting the weights and biases in the backward directionc.The mathematical formula that is used to update the biases based on the gradient descentd.The process of adjusting the weights and biases in the forward direction

What is the process of adjusting the weights and biases based on the error in the output layer called?Select one:a.Activationb.Backpropagationc.None of the aboved.Forward propagation

What are the weights in the backpropagation algorithm?Select one:a.The values that are used to determine the activation function of the neural networkb.The values that are adjusted in the backward passc.The values that are adjusted in the forward passd.The values that are used to determine the output of the neural network

In the weight update rule of the neural network, w  = w - a \delta w1 pointa is called learning ratea is called Hebbian constanta is called momentumNone of the above