Auto-eD: A visual learning tool for automatic differentiation
Lindsey S. Brown, Rachel Moon, David Sondak · Journal of Open Source Education · 2022
Most fields of scientific inquiry require the evaluation of derivatives to calculate and optimize quantities of interest.Automatic differentiation is a set of techniques that allow the differentiation of computer programs to machine precision without requiring full symbolic derivatives (Baydin et al., 2017;Griewank, 1989).The great success of machine learning algorithms, and neural networks in particular, was partly enabled by the celebrated backpropagation algorithm (Werbos, 1990), which is a special case of automatic differentiation.Given the rapidly increasing interest in algorithms that rely on automatic differentiation, and the evolution towards differential programming paradigms (Innes et al., 2019), it is important that students be taught the basics of this key family of algorithms.