62,946 packages matching “automatic differentiation”
@thi.ng/dual-algebra
v1.0.48 · 23 days ago
Multivariate dual number algebra, automatic differentiation
No known vulnerabilities
adnn
v2.0.10 · 8 years ago
Javascript neural networks on top of general scalar/tensor reverse-mode automatic differentiation.
No known vulnerabilities
@danielsimonjr/mathts-autograd
v0.3.13 · 1 month ago
Forward + reverse-mode automatic differentiation for MathTS rank-N Tensor
No known vulnerabilities
autodiff
v2.0.3 · 8 years ago
A Javascript library for performing automatic differentiation
No known vulnerabilities
@tangent.to/grad
v0.2.0 · 14 days ago
Reverse-mode automatic differentiation for JavaScript (ESM): array-valued tape, linear-algebra adjoints (Cholesky, triangular solve, log-determinant). Finite-difference validated.
No known vulnerabilities
@stopcock/autodiff
v2.1.0 · 18 days ago
Reverse-mode automatic differentiation for scalar, vector, and matrix values.
No known vulnerabilities
ad.js
v1.4.1 · 11 years ago
Automatic Differentiation for Javascript
No known vulnerabilities
scalar-autograd
v0.1.9 · 8 months ago
Scalar-based reverse-mode automatic differentiation in TypeScript.
No known vulnerabilities
@johnhenry/math-plus-tensor-autograd
v0.0.0 · 26 days ago
Reverse-mode automatic differentiation over @johnhenry/math-plus-tensor-core Tensors
No known vulnerabilities
autograd-ts
v0.1.5 · 2 months ago
A tiny reverse-mode automatic differentiation engine in TypeScript
No known vulnerabilities
@justinelliottcobb/amari-wasm
v0.24.1 · 1 month ago
WebAssembly bindings for Amari mathematical computing library - geometric algebra, tropical algebra, automatic differentiation, measure theory, fusion systems, and information geometry
No known vulnerabilities
simplegrad
v1.0.0 · 4 years ago
Simple reverse mode automatic differentiation of scalar values in javascript
No known vulnerabilities
adnn.ts
v1.0.1 · 2 years ago
adnn provides TypeSafe Javascript-native neural networks on top of general scalar/tensor reverse-mode automatic differentiation. You can use just the AD code, or the NN layer built on top of it. This architecture makes it easy to define big, complex numer
No known vulnerabilities
@justinelliottcobb/amari-core
v0.3.0 · 11 months ago
Advanced mathematical computing library with geometric algebra, tropical algebra, and automatic differentiation for JavaScript/TypeScript
No known vulnerabilities
gradix
v0.0.1 · 2 years ago
automatic differentiation for javascript
No known vulnerabilities
viff-contracts
v1.1.28 · 3 years ago
Contracts for aligning the Visual Differentiable Programming framework with the Automatic Differentiation Backend.
No known vulnerabilities
gradiatorjs
v0.2.2 · 1 year ago
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
No known vulnerabilities
@auriel/sympjs
v0.0.5 · 8 months ago
A TypeScript library for symbolic mathematics inspired by SymPy, providing symbolic computation, automatic differentiation, and beautiful mathematical rendering.
No known vulnerabilities
autodiff-ts
v0.0.8 · 1 year ago
## 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
No known vulnerabilities
worker-timers
v8.0.34 · 1 month ago
A replacement for setInterval() and setTimeout() which works in unfocused windows.
No known vulnerabilities