ReverseDiff is a fast and compile-able tape-based reverse mode automatic differentiation (AD) that implements methods to take gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really). While performance can vary depending on the functions you evaluate, the algorithms implemented by ReverseDiff generally outperform non-AD algorithms in both speed and accuracy.

Features

  • Supports a large subset of the Julia language, including loops, recursion, and control flow
  • User-friendly API for reusing and compiling tapes
  • Compatible with ForwardDiff, enabling mixed-mode AD
  • Built-in definitions leverage the benefits of ForwardDiff's Dual numbers (e.g. SIMD, zero-overhead arithmetic)
  • Familiar differentiation API for ForwardDiff users
  • Non-allocating linear algebra optimizations
  • Suitable as an execution backend for graphical machine learning libraries

Project Samples

ReverseDiff Screenshot 1

Project Activity

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Categories

Data Visualization

License

MIT License

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ReverseDiff Web Site

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Additional Project Details

Programming Language

Julia

Related Categories

Julia Data Visualization Software

Registered

2023-11-10
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