Forward Pass Rule

The Forward Pass Rule describes how data moves through a neural network to produce an output. It involves passing input information through layers, where each layer applies mathematical functions to transform the data. These transformations are guided by weights (parameters that learn patterns) and biases (adjustments). Starting from the input, each layer processes and passes its result to the next, ultimately generating a prediction or decision at the end. This process is called a “forward pass” because the data flows forward through the network, enabling it to make predictions based on learned patterns.