What is the process of applying an activation function to the dot product of the input layer and the weights called?Select one:a.Activationb.Backpropagationc.None of the aboved.Forward propagation

What role does the activation function play in the back-propagation algorithm for training multilayer feed-forward neural networks? a. It defines the initial weights of the network. b. It calculates the error between predicted and actual outputs. c. It determines the learning rate during weight updates. d. It introduces non-linearity to the network and aids in capturing complex patterns.

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 is the input layer in the backpropagation algorithm?Question 8Answera.The layer that determines the activation function of the neural networkb.The layer that is not visible to the user and processes the input datac.The layer that receives the input data and passes it to the hidden layerd.The layer that produces the final output of the neural network

What is forward propagated in a neural network?1 pointWeights and biasesSumming weightInputActivation function

What is the forward pass in the backpropagation algorithm?Select one:a.The process of adjusting the weights and biases in the backward directionb.The process of calculating the error between the predicted output and the actual outputc.The process of predicting the output of the neural network based on the input datad.The process of adjusting the weights and biases in the forward direction