apache2buddy
apache2buddy (by richardforth)
MLJ.jl
A Julia machine learning framework (by alan-turing-institute)
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| apache2buddy | MLJ.jl | |
|---|---|---|
| 1 | 6 | |
| 380 | 1,720 | |
| - | 1.2% | |
| 3.4 | 8.8 | |
| 9 months ago | 9 days ago | |
| Perl | Julia | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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.
apache2buddy
Posts with mentions or reviews of apache2buddy.
We have used some of these posts to build our list of alternatives
and similar projects.
MLJ.jl
Posts with mentions or reviews of MLJ.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-30.
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What is the Julia equivalent of Scikit-Learn?
MLJ.jl is a good Julia ML framework. There's also a Scikitlearn.jl but its more of a wrapper around the sklearn I believe
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My experience working as a technical writer for MLJ
MLJ is a machine learning framework for Julia, which you can kind of infer from the article but it's not super obvious IMO.
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[N] New BetaML v0.8: model definition, hyperparameters tuning and fitting in 2 lines
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, with a detailed tutorial on its usage from Python or R (no wrapper packages are needed) and an extensive interface to MLJ.
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Python vs Julia
You should definitely go with Julia. It has steeper learning curve than python, but it is way more powerful. As for the ecosystem, you shouldn't worry about that much: DataFrames.jl and friends is way better than pandas, MLJ.jl (https://github.com/alan-turing-institute/MLJ.jl) and FastAI.jl(https://github.com/FluxML/FastAI.jl) are great frameworks for regular ML and deepnet. And if at any point you get a feeling that you need some python library, you can always plug it in with PyCall.jl(https://github.com/JuliaPy/PyCall.jl).
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sklearn equivalent for Julia?
Imho, Julia is more diverse in the sense that there is not a single popular ML library. Maybe the Julian equivalent for scikit-learn is MLJ.jl. There is also ScikitLearn.jl, which defines the usual interface of scikit-learn models, and specific algorithms then implement this interface.
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Swift for TensorFlow Shuts Down
Then you haven't looked at Julia's ecosystem.
It may not be quite as mature, but it's getting there quickly.
It's also far more interoperable because of Julia's multiple dispatch and abstract types.
For example, the https://github.com/alan-turing-institute/MLJ.jl ML framework (sklearn on steroids), works with any table object that implements the Tables.jl interface out of the box, not just with dataframes.
That's just one example.