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 the input layer of a feedforward neural network called?Question 6Answera.Hidden layerb.Output layerc.None of the aboved.Input layer

What is the backpropagation algorithm used for?Question 10Answera.To optimize the activation function of a neural networkb.To find the optimal weights and biases of a neural networkc.To classify data using a neural networkd.All of the above

Question textHow does a backpropagation network learn?Select one:a.By adjusting the number of neurons in the network based on the input and output datab.By adjusting the weights and biases of the network based on the input and output datac.By adjusting the activation function of the network based on the input and output datad.By adjusting the architecture of the network based on the input and output data

What is the primary purpose of the backpropagation algorithm in training a neural network?ATo compute the output of the networkBTo initialize the weights of the networkCTo update the weights of the network by minimizing the loss functionDTo determine the optimal network architecture

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.