Match each terminology with the appropriate explanation for forward propagation method in neural network. Pair up the right answers for all pairs:

$1.$ Activation Function

$2.$ Input Layer

$3.$ Hidden Layers

$4.$ Weights and Biases

$5.$ Weighted Sum

$6.$ Output Layer

$7.$ Final Output

A$.$ The output layer produces the final prediction or output of the neural network based on the input data.

B$.$ Each connection between neurons in the neural network has an associated weight and bias.

C$.$ The input layer consists of the input features or variables, representing information about the input data.

D$.$ If the neural network has hidden layers, the input data is passed through these layers before reaching the output layer.

E$.$ In each neuron, the inputs from the previous layer are multiplied by the corresponding weights and summed up along with the bias term.

F$.$ After the weighted sum is calculated, an activation function is applied to introduce non$-$linearity into the network.

G$.$ The output could represent the probability of belonging to a certain class in a binary classification task