Forward propagation is the process by which we attempt to predict our target variable using the features present in a single observation. Imagine we had a two-layer neural network. In the forward propagation process, we would start with the features present within that observation  and then multiply those features by their associated coefficients within layer 1 and add a bias term for each neuron. After that, we would send that output to the activation for the neuron. Following that, the output would be sent to the next layer, and so on, until we reach the end of the network where we are left with our network's prediction:

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