DiffSharp
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
What is DiffSharp?
DiffSharp is a tensor library for differentiable programming, providing automatic differentiation capabilities for machine learning, optimization, and probabilistic programming. It integrates F# functional programming with efficient tensor operations via LibTorch backends, supporting GPU acceleration and cross-platform development.
Machine learning researchers, data scientists, and F# developers working on optimization, probabilistic models, or scientific computing who need automatic differentiation in a functional programming environment.
Developers choose DiffSharp for its combination of F#'s type safety and functional paradigms with PyTorch-like APIs and high-performance tensor operations, offering a unique, robust tool for differentiable programming in the .NET ecosystem.
Overview
DiffSharp: Differentiable Functional Programming
Use Cases
Best For
- Implementing automatic differentiation in F# projects
- Building machine learning models with functional programming patterns
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