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
Categories
Data VisualizationLicense
MIT LicenseFollow ReverseDiff
You Might Also Like
MongoDB Atlas runs apps anywhere
MongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of ReverseDiff!