Tutorial 3-Forward Propogation In MultiLayered Neural Network- Krish Naik Hindi

Forward propagation is where input data is fed through a network, in a forward direction, to generate an output. The data is accepted by hidden layers and processed, as per the activation function, and moves to the successive layer. The forward flow of data is designed to avoid data moving in a circular motion, which does not generate an output.

During forward propagation, pre-activation and activation take place at each hidden and output layer node of a neural network. The pre-activation function is the calculation of the weighted sum. The activation function is applied, based on the weighted sum, to make the neural network flow non-linearly using bias.
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