numhask
A haskell numeric prelude, providing a clean structure for numbers and operations that combine them. (by tonyday567)
ad
Automatic Differentiation (by ekmett)
Our great sponsors
| numhask | ad | |
|---|---|---|
| - | 6 | |
| 67 | 364 | |
| - | - | |
| 6.8 | 5.5 | |
| 2 months ago | 1 day ago | |
| Haskell | Haskell | |
| GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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.
numhask
Posts with mentions or reviews of numhask.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning numhask yet.
Tracking mentions began in Dec 2020.
ad
Posts with mentions or reviews of ad.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-02-27.
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Is there an implementation of The Simple Essence of Automatic Differentiation (2018)?
Maybe ad?
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Backpropagation and Accelerate
I’ll also link the ad package here in case someone can speak to its value over backprop https://github.com/ekmett/ad
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[ad] Haskell Revitalisation
Apologies in advance for disappointing a few people but the [ad] part in the title doesn't mean Automatic Differentiation but rather means "advertisement".
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Let's Program a Calculus Student II: Turning Symbolic Differentiation Automatic
Hi everybody! A couple weeks ago, I made a blog post talking about how recursion and pattern matching could be used to translate Calculus formulas into Haskell. This is a follow-up exploring how to use automatic differentiation to calculate those same derivatives as an example of cool stuff that polymorphism allows us to do. (I learned this idea from the ad package and really fell in love with how elegant it is)
- Monthly Hask Anything (March 2022)
- What are some ways I could tickle my (beginner) haskell-brain with something *useful*?
What are some alternatives?
When comparing numhask and ad you can also consider the following projects:
nuha
modular-arithmetic - A useful type for working with integers modulo some constant.
deeplearning-hs
roots - 1-dimensional root-finding algorithms in Haskell
dvda - (deprecated) Symbolic expressions and algorithmic differentiation in Haskell.
nimber - Finite nimber arithmetic
tower - Deprecated in favour of https://github.com/tonyday567/numhask
search - infinite search in finite time with Hilbert's epsilon
jalla - Just another library for linear algebra (Haskell)
gamma - Haskell implementation of gamma and incomplete gamma functions
noether - Highly polymorphic algebraic structures with custom deriving strategies
moving-averages