A Javascript library for performing automatic differentiation
Tired of doing math to get normals in your vertex shader? Same. Use this library to write your function once and generate derivatives automatically!
## Overview `autodiff-ts` is a TypeScript implementation of automatic differentiation. Automatic Differentiation (AD) [^1] is a technique for computationally determining the gradient of a function with respect to its inputs. It strikes a balance between t
Autodiff of Wasm GC.
This is a javascript implementation of autodiff translated from micrograd by Adrej Karpathy. Then built upon this to build a torch like neural network composition
Oxide-JS Core: Matrix operations and Rust native acceleration.
GradiatorJS is a lightweight, from-scratch autodiff engine and a neural network library written in typescript. Featuring a powerful automatic differentiation engine using a computation graph to enable backpropagation on dynamic network architectures. You
[](https://www.npmjs.com/package/@2bad/micrograd) [](https://opensource.org/license/MIT) [ and reverse-mode (tape) for gradients and Jacobians.
Procedural macros for bevy_autodiff automatic differentiation
automatic differentiation with optional SIMD acceleration
A library for easily adding automatic differentiation to Rust, even on already written code.
Flexible and Comprehensive Deep Learning Framework in Rust
A library for easily adding automatic differentiation to Rust, even on already written code.
A library for easily adding automatic differentiation to Rust, even on already written code.
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