what is forward propagation in neural network

This recipe explains what is forward propagation in neural network

Recipe Objective - What is forward propagation?

Forward propagation refers to storage and calculation of input data which is fed in forward direction through the network to generate an output. Hidden layers in neural network accepts the data from the input layer, process it on the basis of activation function and pass it to the output layer or the successive layers. Data flows in forward direction so as to avoid circular shape flow of data which will not generate an output. The network configuration that helps in forward propagation is known as feed-forward network.

Explanation of Forward propagation.

During forward propagation, preactivation and activation takes place at each hidden layer and output layer node of neural network.

Preactivation is weighted sum of the inputs that is the linear transformation of the weights with respect to the available inputs. A neuron makes decision whether to pass the information further or not based on the sum and the activation function during forward propagation.

Activation is calculated weighted sum of the inputs passed to activation function. An activation function is the mathematical function that adds non-linearity to network.

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