Backpropagation is a fundamental algorithm used in training artificial neural networks, enabling them to learn from data. It involves a two-step process: first, the network makes predictions based on input data, and then it calculates the error by comparing these predictions to the actual results. During the backpropagation phase, this error is propagated backward through the network, allowing the algorithm to adjust the weights of the connections between neurons. This fine-tuning enhances the model’s accuracy over time, making backpropagation essential for developing effective machine learning models. Understanding this process is crucial for anyone interested in artificial intelligence and neural networks.