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

See All Activity >

Categories

Data Visualization

License

MIT License

Follow ReverseDiff

ReverseDiff Web Site

Other Useful Business Software
Already Paying for an RMM? Get 50% Off Splashtop AEM Icon
Already Paying for an RMM? Get 50% Off Splashtop AEM

Switch to Splashtop AEM and save up to 50% in your first year

Automated OS and third-party patching for Windows and macOS, CVE prioritization with AI patch summaries, and built-in remote control that also supports Linux. Qualifying organizations switching from another RMM get up to 50% off year one.
Get Started
Rate This Project
Login To Rate This Project

User Reviews

Be the first to post a review of ReverseDiff!

Additional Project Details

Programming Language

Julia

Related Categories

Julia Data Visualization Software

Registered

2023-11-10
Report inappropriate content