Backpropagation, a fundamental algorithm in training artificial neural networks, has a rich history spanning several decades. From its early conceptual roots in the 1960s to its formal development in the 1970s and widespread adoption in the 1980s, backpropagation has played a crucial role in advancing machine learning and artificial intelligence. This algorithm, which efficiently calculates gradients to adjust network weights and minimize errors, has enabled the training of complex neural networks and underpinned many breakthroughs in deep learning.