Forward Propagation

Forward propagation is the process in a neural network where input data moves through the model to produce an output. Think of it like passing information through a series of steps: each step transforms the data using mathematical functions and parameters (called weights and biases). The data is gradually refined until it reaches the final layer, producing a prediction or result. This process allows the neural network to analyze complex patterns in data, like recognizing images or understanding language, by systematically processing information from input to output.