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Feedforward Propagation

In deep learning, feedforward propagation is one of the two sub-processes of the training process, which builds correlation by assigning parameters.

Feedforward Propagation occurs when the input data is fed in the forward direction through the network. Each hidden layer receives the input data, processes it (using an Activation Function), and passes it onto the next layer.

In the feedforward propagation, the Activation Function is a mathematical “gate” in between the input feeding the current neuron and its output going to the next layer.

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