owl VS mercury-ad

Compare owl vs mercury-ad and see what are their differences.

owl

Owl - OCaml Scientific Computing @ https://ocaml.xyz (by owlbarn)
Matrix Linear Algebra Ndarray statistical-functions topic-modeling Regression Maths GSL Plotting sparse-linear-systems Scientific Computing numerical-calculations Statistics Mcmc Optimization Autograd algorithmic-differentation automatic-differentiation Machine Learning neural-network
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mercury-ad

Mercury library for automatic differentiation (by mclements)
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owl mercury-ad
5 2
1,179 6
0.8% -
8.2 10.0
4 days ago over 1 year ago
OCaml Mercury
MIT License GNU General Public License v3.0 only
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

owl

Posts with mentions or reviews of owl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-24.

mercury-ad

Posts with mentions or reviews of mercury-ad. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-08-24.
  • Understanding Automatic Differentiation in 30 lines of Python
    9 projects | news.ycombinator.com | 24 Aug 2023
    I wrote a purely functional AD library in Mercury [0], which adapts a general approach from [1]. I believe that Owl provides a similar approach [2].

    [0] https://github.com/mclements/mercury-ad

  • Autodidax: Jax Core from Scratch (In Python)
    4 projects | news.ycombinator.com | 11 Feb 2023
    I find the solutions from https://github.com/qobi/AD-Rosetta-Stone/ to be very helpful, particularly for representing forward and backward mode automatic differentiation using a functional approach.

    I used this code as inspiration for a functional-only (without references/pointers) in Mercury: https://github.com/mclements/mercury-ad

What are some alternatives?

When comparing owl and mercury-ad you can also consider the following projects:

Octavian.jl - Multi-threaded BLAS-like library that provides pure Julia matrix multiplication

GPU-Puzzles - Solve puzzles. Learn CUDA.

Arraymancer - 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

AD-Rosetta-Stone - Examples of Automatic Differentiation (AD) in many different languages and systems

Peroxide - Rust numeric library with R, MATLAB & Python syntax

autodidact - A pedagogical implementation of Autograd

symengine.rs - (Unofficial) Rust wrappers to the C++ library SymEngine, a fast C++ symbolic manipulation library.

autograd - Efficiently computes derivatives of numpy code.

db-benchmark - reproducible benchmark of database-like ops

micrograd - A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

sparse-linear-algebra - Numerical computation in native Haskell

owl vs Octavian.jl mercury-ad vs GPU-Puzzles owl vs Arraymancer mercury-ad vs AD-Rosetta-Stone owl vs Peroxide mercury-ad vs autodidact owl vs symengine.rs mercury-ad vs autograd owl vs db-benchmark owl vs GPU-Puzzles owl vs micrograd owl vs sparse-linear-algebra