The Simple Essence of Automatic Differentiation - Conal Elliott

Microsoft ResearchLength: 1 hour 30 minutesMachine learning & deep learning

In brief

Conal Elliott presents his paper on automatic differentiation, deriving a simple, generalized AD algorithm from a natural specification. Varying the representation of derivatives yields forward and reverse mode algorithms that avoid the complexity and statefulness of backpropagation.