Backpropagation is crucial in training artificial neural networks to learn and improve their performance over time. The basic idea behind backpropagation is to propagate the error of the output back through the network, to let it adjust itself (learn) in a way that reduces the error in the future. It is a key component of many popular applications, including image and speech recognition, natural language processing, and financial modeling. Pixis AIs use backpropagation to decrease error rates and make better recommendations for improved efficiency.
