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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
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Data VisualizationLicense
MIT LicenseFollow ReverseDiff
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