BC Architecture

BC (Backpropagation) architecture is a method used to train neural networks, which are computer systems modeled after the human brain. It involves two main steps: first, the network makes a prediction based on input data; second, the error or difference between the prediction and the actual result is calculated. This error is then propagated backward through the network, allowing each connection to adjust and improve accuracy over time. Essentially, BC helps the network learn from mistakes by fine-tuning its internal settings, enabling more accurate predictions in tasks like recognition, classification, and decision-making.