Beyond automatic differentiation – Google AI Blog

Derivatives play a central role in optimization and machine learning. Automatic differentiation frameworks such as TensorFlow, PyTorch, and JAX are an essential part of modern machine learning, making it feasible to use gradient-based optimizers to train very complex models. But are derivatives all we need? By themselves, derivatives only tell us how a function behaves on an infinitesimal scale. To use derivatives effectively, we often need to know more than that.