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
Categories
Data VisualizationLicense
MIT LicenseFollow ReverseDiff
Other Useful Business Software
Build Securely on Azure with Proven Frameworks
Moving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of ReverseDiff!