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Articles by using Pkg
Julia编程04：Julia调用R和Python
参考使用homebrew管理linux和mac软件安装homebrew、mamba和Python Python环境 mamba create -y -n jlpy python=3.10 mamba activate jlpy mamba install matplotlib which python # /opt/homebrew/Caskroom/mambaforge/base/envs/jlpy/bin/python R环境 在R中运行，找到R_HOME R.home() # /Library/Frameworks/R.framework/Resources Build 在Julia中运行 using Pkg Pkg.add(["PyCall","RCall"]) ENV["PYTHON"]="/opt/homebrew/Caskroom/mambaforge/base/envs/jlpy/bin/python" Pkg.build("PyCall") ENV["R_HOME"] = "/Library/Frameworks/R.framework/Resources"...
Meet Julia: The Future of Data Science
The next big thing, or just massively overhyped? As a data enthusiast, you have probably heard about Julia, the “future programming language of data science.” There are claims that Julia will replace Python and R in the data science field, as it offers significant benefits in terms of performance, efficiency, and ease of use. In this article, we will look into what Julia is, its applications, and whether it is worth learning the language for data science.
Eror UndefVarError: plot not defined β JuliaLang
In this short blog post, you will two useful tips to fix a very common Julia errorπ π Rebuild Plots Package In order to fix the issue, use the following code. using Pkg Pkg.build("Plots") That will rebuild the package. Note that the actual compiling of the packages is just in time so they are complied when you load them. β€΅οΈ Properly Import Plots Package In order to plot your functions, use the following commands before you execute your actual plot commands.
10-Minute Guide to Julia For Die-Hard Python Lovers
Photo by Emma Bauso on Pexels Don't get me wrong. I am not a Python hater. I love it, and my eyebrows will probably turn blue before that fact changes. But Julia seems too good to be true. All programming languages are excellent at specific tasks while being terrible at others. The creators of Julia were greedy — they wanted all the superpowers of the best languages and to create a super language. They did.
How To Use PyCall.jl: Python Libraries In Julia
A confession I must make is that I have not used the PyCall.jl package in a long time. Frankly, Julia’s ecosystem has grown dramatically since the last time I touched the package, and so has the language’s user-base.
Are Your Playlists Groovy Enough?
Photo by Blaz Photo on Unsplash Recently, I came across some excellent articles where the authors had made use of Spotify’s API to analyze their music data. This made me very curious, and I decided to try doing something similar using Julia. Thankfully, the wrapper for the API has already been implemented in Spotify.jl, but the package isn’t registered yet. That’s of course not an issue, we can always install it directly from its GitHub repository.
Simple Image Classification Using FastAI.jl
The Fastai library is now on Julia with similar features available in Python. In this project, we are going to train the Resnet-18 model to classify images from the ImageNet dataset in few steps. The FastAI.jl library is similar to the fast.ai library in Python and it’s the best way to experiment with your deep learning projects in Julia. The library allows you to use state-of-the-art models that you can modify, train, and evaluate by using few lines of code.
Fantastic Tips For The Julia REPL
(image by author) Julia is a really cool programming language that has brought a lot of very interesting concepts into the forefront of many developers and Scientists mind over the past few years. Concepts such as Just In Time (JIT) compilation and multiple dispatch have now become an enormous talking point in both the Data Science and general programming communities, and this is for good reason because Julia really does execute these concepts very well.
A comprehensive study of Mixed Integer Programming with JuMP on Julia (Part 1)
Photo by Antoine Dautry on Unsplash One of the primary purposes of the computer sciences and operation research is to solve problems efficiently; problem-solving is a field where we often find very “ad-hoc” methods of resolution, they can be efficient, but they rely on some specific properties of the problem which are not necessarily easy to notice.
Deploying Julia projects on Heroku.com
Hi! Today I will be showing you how you can deploy projects in the Julia Programming Language on Heroku, a cloud application platform. In this example, I’ll be deploying a Dashboards.jl file to demonstrate. Everything from this blog is from my GitHub repository and the final product is here.
Three Julia Editors
Visual Studio Code, Juno and Jupyter Notebooks are all great environments for developing Julia code. Screenshots of the three IDEs. All images by the author except where noted. Julia is a young language and so support is not as comprehensive as it is for other more established languages such as Java or Python. However, while there may not be a vast number of development environments available for it, you do have the choice of some good ones.
Why Use Julia Programming Language
Explore the advantages of Julia for high-performance programming and data science applications. The framework for autonomous intelligence Julia is designed for high-performance numerical and scientific computing, making it an excellent choice for data engineering tasks that require speed and efficiency. Its just-in-time (JIT) compilation allows for execution speeds comparable to C, which is a significant advantage over interpreted languages like Python.
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