Top 19 Julia HacktoberFest Projects
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2) Flux treats softmax a little different than most other activation functions (see here for more details) such as relu and sigmoid. When you pass an activation function into a layer like Dense(3, 32, relu), Flux expects that the function is broadcast over the layer's output. However, softmax cannot be broadcast as it operates over vectors rather than scalars. This means that if you want to use softmax as the final activation in your model, you need to pass it into Chain() like so:
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Project mention: Can I say Makie and Vegalite is the best pure Julia data visulization library yet? | reddit.com/r/Julia | 2021-11-28
I haven't heard anything about that. They had their latest release in October, 2021 according to GitHub (but I don't follow it very closely, so maybe they have mentioned somewhere that they are winding down?).
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SonarLint
Clean code begins in your IDE with SonarLint. Up your coding game and discover issues early. SonarLint is a free plugin that helps you find & fix bugs and security issues from the moment you start writing code. Install from your favorite IDE marketplace today.
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Have a look at the source: https://github.com/JuliaPlots/Plots.jl/blob/master/src/animation.jl It shouldn't be too hard to write something specifically for PlotlyJS yourself.
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DataFrames.jl and XLSX.jl for JSON, CSV, and XLSX files
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If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.
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Here you go: https://github.com/JuliaAnimators/Javis.jl
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Jullia has packages too.
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Scout APM
Less time debugging, more time building. Scout APM allows you to find and fix performance issues with no hassle. Now with error monitoring and external services monitoring, Scout is a developer's best friend when it comes to application development.
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OrdinaryDiffEq.jl
High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Project mention: How do the Julia ODE solvers choose/select their initial steps? What formula do they use to estimate the appropriate initial step size? | reddit.com/r/Julia | 2021-12-15Yes. If you want to see a robust version of the algorithm you can check out https://github.com/SciML/OrdinaryDiffEq.jl/blob/master/src/initdt.jl
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ChainRules.jl
forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs
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Project mention: Because cross-compiling binaries for Windows is easier than building natively | news.ycombinator.com | 2022-06-18
There is the Julia package https://github.com/JuliaPackaging/BinaryBuilder.jl which creates an environment that fakes being another, but with the correct compilers and SDKs . It's used to build all the binary dependencies
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There is the rebooted version https://github.com/JuliaGraphs/Graphs.jl.
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If you strongly prefer to run scripts though, then you can use the package https://github.com/dmolina/DaemonMode.jl in order to re-use a Julia session between multiple scripts, saving you recompilation time.
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SciMLSensitivity.jl
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.
Project mention: Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation - Chris Rackauckas (ASA Statistical Computing & Graphics Sections) | reddit.com/r/Julia | 2022-08-07Plenty of code examples! https://sensitivity.sciml.ai/dev is the main resource, but most of the papers mentioned have their own code repositories. I'm trying to get most of them updated and into the larger SciMLSensitivity docs so they are all tested, though we need new hosting computers to actually get that done.
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AlphaVantage has some of the feeds you are interested in and is free: https://www.alphavantage.co/ and I help maintain the Julia interface to AlphaVantage https://github.com/ellisvalentiner/AlphaVantage.jl
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Julia HacktoberFest related posts
- Julia and GPU processing, how does it work?
- In my experience, Julia and its packages have the highest rate of serious correctness bugs of any programming system I’ve used, and I started programming with Visual Basic 6 in the mid-2000s.
- Discussion Thread
- Animating plots using only PlotlyJS ?
- Processing/p5.js like environment for Julia ?
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
- C++ Manim?
Index
What are some of the best open-source HacktoberFest projects in Julia? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Flux.jl | 3,781 |
| 2 | Gadfly.jl | 1,788 |
| 3 | Plots.jl | 1,585 |
| 4 | DataFrames.jl | 1,429 |
| 5 | CUDA.jl | 818 |
| 6 | Javis.jl | 713 |
| 7 | Agents.jl | 457 |
| 8 | OrdinaryDiffEq.jl | 368 |
| 9 | ChainRules.jl | 313 |
| 10 | BinaryBuilder.jl | 289 |
| 11 | www.julialang.org | 283 |
| 12 | Graphs.jl | 258 |
| 13 | HypothesisTests.jl | 233 |
| 14 | DaemonMode.jl | 216 |
| 15 | Yggdrasil | 208 |
| 16 | SciMLSensitivity.jl | 191 |
| 17 | GPUCompiler.jl | 80 |
| 18 | AlphaVantage.jl | 65 |
| 19 | Curry.jl | 13 |
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