Backpropagation is a fundamental algorithm used in machine learning for training neural networks. It involves propagating the error back through the network to update the weights and biases, thereby minimizing the error in predictions. This algorithm is crucial for improving the performance of models, making it particularly useful in complex fields such as toxicology.
