mercury-ad
Mercury library for automatic differentiation (by mclements)
AD-Rosetta-Stone
Examples of Automatic Differentiation (AD) in many different languages and systems (by qobi)
| mercury-ad | AD-Rosetta-Stone | |
|---|---|---|
| 2 | 2 | |
| 6 | 26 | |
| - | - | |
| 10.0 | 10.0 | |
| over 1 year ago | almost 6 years ago | |
| Mercury | Scala | |
| GNU General Public License v3.0 only | 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.
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.
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
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)
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
AD-Rosetta-Stone
Posts with mentions or reviews of AD-Rosetta-Stone.
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
[1] https://github.com/qobi/AD-Rosetta-Stone/
-
Autodidax: Jax Core from Scratch (In Python)
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 mercury-ad and AD-Rosetta-Stone you can also consider the following projects:
GPU-Puzzles - Solve puzzles. Learn CUDA.
autograd - Efficiently computes derivatives of numpy code.
autodidact - A pedagogical implementation of Autograd
Tensor-Puzzles - Solve puzzles. Improve your pytorch.
owl - Owl - OCaml Scientific Computing @ https://ocaml.xyz