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Search results for automatic differentiation
automatic-differentiation
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293 search results found
Ggml
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8,676
Tensor library for machine learning
Gorgonia
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5,357
Gorgonia is a library that helps facilitate machine learning in Go.
Mindspore
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3,915
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Pennylane
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2,164
PennyLane is a cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Train a quantum computer the same way as a neural network.
Tangent
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2,066
Source-to-Source Debuggable Derivatives in Pure Python
Spago
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1,691
Self-contained Machine Learning and Natural Language Processing library in Go
Autodiff
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1,454
automatic differentiation made easier for C++
Zygote.jl
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1,415
21st century AD
Pinocchio
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1,331
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
Effectivepytorch
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1,321
PyTorch tutorials and best practices.
Owl
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1,178
Owl - OCaml Scientific Computing @ https://ocaml.xyz
Arraymancer
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1,177
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
Enzyme
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1,142
High-performance automatic differentiation of LLVM and MLIR.
Aesara
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1,122
Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.
Control Toolbox
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838
The Control Toolbox - An Open-Source C++ Library for Robotics, Optimal and Model Predictive Control
Forwarddiff.jl
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833
Forward Mode Automatic Differentiation for Julia
Deeplearning.scala
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759
A simple library for creating complex neural networks
Optim
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726
OptimLib: a lightweight C++ library of numerical optimization methods for nonlinear functions
Math
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700
The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.
Pymanopt
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668
Python toolbox for optimization on Riemannian manifolds with support for automatic differentiation
Optimization.jl
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625
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Aerosandbox
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573
Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
Tullio.jl
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559
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Qml
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490
Introductions to key concepts in quantum programming, as well as tutorials and implementations from cutting-edge quantum computing research.
Kotlingrad
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489
🧩 Shape-Safe Symbolic Differentiation with Algebraic Data Types
Deepdarkfantasy
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464
A Programming Language for Deep Learning
Torchopt
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460
TorchOpt is an efficient library for differentiable optimization built upon PyTorch.
Gtn
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443
Automatic differentiation with weighted finite-state transducers.
Grassmann.jl
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429
⟨Grassmann-Clifford-Hodge⟩ multilinear differential geometric algebra
Deep Learning From Scratch 3
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426
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
Ott
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417
Optimal Transport tools implemented with the JAX framework, to get auto-diff, parallel and jit-able computations.
Rust Autograd
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413
Tensors and differentiable operations (like TensorFlow) in Rust
Chainrules.jl
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404
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
Enzyme.jl
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364
Julia bindings for the Enzyme automatic differentiator
Ad
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350
Automatic Differentiation
Tinyad
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329
Automatic Differentiation in Geometry Processing Made Simple
Reversediff.jl
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327
Reverse Mode Automatic Differentiation for Julia
Betty
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316
Betty: an automatic differentiation library for generalized meta-learning and multilevel optimization
Theano_lstm
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309
🔬 Nano size Theano LSTM module
Tensorlang
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303
Tensorlang, a differentiable programming language based on TensorFlow
Mitgcm
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302
M.I.T General Circulation Model master code and documentation repository
Surrogates.jl
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302
Surrogate modeling and optimization for scientific machine learning (SciML)
Taylorseries.jl
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299
Taylor polynomial expansions in one and several independent variables.
Emmy
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255
The Emmy Computer Algebra System.
Chainrulescore.jl
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237
AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any particular AD system.
Kayak
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231
Kayak is a library for automatic differentiation with applications to deep neural networks.
Tensorcircuit
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230
Tensor network based quantum software framework for the NISQ era
Nilang.jl
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223
A differential eDSL that can run faster than light and go back to the past.
Neuraloperators.jl
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206
DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
Xad
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203
Comprehensive automatic differentiation in C++
Jaxfluids
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203
Differentiable Fluid Dynamics Package
Integrals.jl
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191
A common interface for quadrature and numerical integration for the SciML scientific machine learning organization
Nnlib.jl
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187
Neural Network primitives with multiple backends
Backprop
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180
Heterogeneous automatic differentiation ("backpropagation") in Haskell
Sinkhornautodiff
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178
Toolbox to integrate optimal transport loss functions using automatic differentiation and Sinkhorn's algorithm
Talk 2018 Essence Of Ad
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166
The simple essence of automatic differentiation
Tensors.jl
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164
Efficient computations with symmetric and non-symmetric tensors with support for automatic differentiation.
Topopt.jl
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163
A package for binary and continuous, single and multi-material, truss and continuum, 2D and 3D topology optimization on unstructured meshes using automatic differentiation in Julia.
Autograd.jl
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160
Julia port of the Python autograd package.
Adcme.jl
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158
Automatic Differentiation Library for Computational and Mathematical Engineering
Omeinsum.jl
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152
One More Einsum for Julia! With runtime order-specification and high-level adjoints for AD
Distributionsad.jl
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146
Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff
Yota.jl
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146
Reverse-mode automatic differentiation in Julia
Mcmc
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143
A C++ library of Markov Chain Monte Carlo (MCMC) methods
Dcpp
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142
Automatic differentiation in C++; infinite differentiability of conditionals, loops, recursion and all things C++
Torchquad
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142
Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX
Jax_cosmo
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138
A differentiable cosmology library in JAX
Tensorflow Forward Ad
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130
Forward-mode Automatic Differentiation for TensorFlow
Nbodysimulator.jl
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122
A differentiable simulator for scientific machine learning (SciML) with N-body problems, including astrophysical and molecular dynamics
Hamilton
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121
Simulate physics on generalized coordinate systems using Hamiltonian Mechanics and automatic differentiation. Don't throw away your shot.
Cppadcodegen
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119
Source Code Generation for Automatic Differentiation using Operator Overloading
Neograd
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117
A deep learning framework created from scratch with Python and NumPy
Implicitdifferentiation.jl
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107
Automatic differentiation of implicit functions
Freetensor
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106
A language and compiler for irregular tensor programs.
Scorch
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104
scorch is a deep learning framework in Scala inspired by PyTorch
Inferopt.jl
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102
Combinatorial optimization layers for machine learning pipelines
Infermo
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102
Tensors and dynamic Neural Networks in Mojo
Notebooks
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101
Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
Nonconvex.jl
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100
Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.
Preallocationtools.jl
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99
Tools for building non-allocating pre-cached functions in Julia, allowing for GC-free usage of automatic differentiation in complex codes
Taylorintegration.jl
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99
ODE integration using Taylor's method, and more, in Julia
Adept
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92
Fast automatic differentiation library in C++
Autoptim
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91
Automatic differentiation + optimization
Tensorgrad
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90
Differentiable Programming Tensor Networks
Opm Simulators
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90
Simulator programs and utilities for automatic differentiation.
Compfinance
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86
Companion code for "Modern Computational Finance: AAD and Parallel Simulations" (Antoine Savine, Wiley, 2018)
Deepnet
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84
Deep.Net machine learning framework for F#
Autograd From Scratch
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82
Documented and Unit Tested educational Deep Learning framework with Autograd from scratch.
Adam
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82
adam implements a collection of algorithms for calculating rigid-body dynamics in Jax, CasADi, PyTorch, and Numpy.
Fastad
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81
FastAD is a C++ implementation of automatic differentiation both forward and reverse mode.
Autonn
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80
A fast and expressive Matlab/MatConvNet deep learning API, with native automatic differentiation
Tensornetworkad.jl
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77
Algorithms that combine tensor network methods with automatic differentiation
Catalyst
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75
A JIT compiler for hybrid quantum programs in PennyLane
Jax_fdm
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74
Auto-differentiable and hardware-accelerated force density method
Dualnumbers.jl
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73
Julia package for representing dual numbers and for performing dual algebra
Jax Fenics Adjoint
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71
Differentiable interface to FEniCS/Firedrake for JAX using dolfin-adjoint/pyadjoint
Exprgrad
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70
An experimental deep learning framework for Nim based on a differentiable array programming language
Multiscalearrays.jl
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69
A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations
Quantumflow
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69
QuantumFlow: A Quantum Algorithms Development Toolkit
Juliacomputation
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68
Repository for Common Ground C25
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