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Simple implementations of forward- and backward-mode automatic differentation in Scala
Functional Deep Learning
This repository contains simple implementations of forward- and backward-mode automatic differentiation, written in Scala, which is the core algorithm in deep learning. There are two goals to this code:
- demonstrate how automatic differentiation works (and hopefully show how simple it is); and
- show that the chain rule forms a monad (which I haven't been able to find mentioned in the literature).