The Training Process: Step by Step
Training a neural network resembles teaching a child through consistent feedback and gradual improvement. Each training step follows a precise sequence that slowly transforms the network from making random guesses to providing accurate predictions.
In each iteration, the model processes examples (forward pass), evaluates its mistakes (loss computation), figures out which connections need adjustment (backpropagation), and refines its knowledge (weight updates). This continuous cycle of prediction, evaluation, and refinement allows the network to gradually discover patterns in the data that may be invisible even to human experts.