Backpropagation was invented in the 1970s as a general optimization method for automatically differentiating complex nested functions. However, it wasn't until 1986, when David Rumelhart, Geoffrey Hinton, and Ronald Williams published a paper in Nature Magazine titled "Learning Representations by Back-Propagating Errors" that the importance of the algorithm was appreciated. The paper introduced its algorithm, explaining how it plays a vital role in deep learning. This machine learning paradigm has revolutionized various fields, including computer vision, natural language processing, and speech recognition. The backpropagation algorithm computes the network's output error…