DataScience
HTTP.jl
DataScience | HTTP.jl | |
---|---|---|
9 | 7 | |
478 | 624 | |
0.0% | 0.8% | |
0.0 | 7.8 | |
about 1 year ago | 5 days ago | |
Jupyter Notebook | Julia | |
MIT License | GNU General Public License v3.0 or later |
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.
DataScience
-
Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
For all topics that explained briefly, I provided the links with more thorough documentation. In addition, I would highly recommend reading the Julia Data Science online book and learn the great set of machine learning examples in Julia Academy Data Science GitHub repository.
- DataScience: NEW Courses - star count:421.0
-
Error message: TypeError
So, I just decided to try to learn Julia, and started by following the Julia for DataScience lectures on JuliaAcademy. In the first lecture, I get instructed to clone the DataScience repository on GitHub. According to instructions, I activated the environment with activate and check the status (status). I then ran instantiate to update any necessary packages, and get the following error message:
HTTP.jl
-
Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
The req.url field contains the URL of the received request, the req.method field contains request method, like GET or POST, the req.body field contains the POST body of the request in binary format. HTTP request object contains much other information. All this you can find in HTTP.jl documentation. Our web application will only check the request method. If the received request is a POST request, it will parse req.body to JSON object and send the data from this object to the isSurvived function to make a prediction and return it to the client browser. For all other request types, it will just return the content of the index.html file, to display the web interface. This is how the whole source of titanic.jl web service looks:
-
How can I use Julia to search on the web automatically?
If you want to just get the html of a website whose url you already have you can make requests from the http.jl package. https://juliaweb.github.io/HTTP.jl/stable/
-
Automate the boring stuff with Julia?
HTTP.jl and Gumbo.jl for web-scraping
- PyTorch: Where we are headed and why it looks a lot like Julia (but not exactly)
-
Recommendations on how to start web scraping with julia for price updates? (if possible)
I haven't seen that tutorial, but I agree that HTTP.jl, Gumbo.jl, and Cascadia.jl are the way. I used them to export public wishlists from bookdepository, which has no API nor a built in exporting tool.
-
Why not Julia?
I find some of the library documentation hard to understand. Compare http.jl with python's requests, for example. Something as core as HTTP requests should have clear docs with tonnes of examples. Part of this is also a personal dislike of documenter.jl styling. Idk why the contrast is so low – would prefer a standard readthedocs theme.
- Julia 1.6: what has changed since Julia 1.0?
What are some alternatives?
Zygote-Mutating-Arrays-WorkAround.jl - A tutorial on how to work around ‘Mutating arrays is not supported’ error while performing automatic differentiation (AD) using the Julia package Zygote.
geni-performance-benchmark
Julia-on-Colab - Notebook for running Julia on Google Colab
julia - The Julia Programming Language
julia_titanic_model - Titanic machine learning model and web service
DaemonMode.jl - Client-Daemon workflow to run faster scripts in Julia
DataFrames.jl - In-memory tabular data in Julia
JET.jl - An experimental code analyzer for Julia. No need for additional type annotations.
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
BinaryBuilder.jl - Binary Dependency Builder for Julia
ThreeBodyBot - Poorly written code that generates moderately exciting plots of a very specific physics phenomenon that enthralls dozens of us around the globe.
PackageCompiler.jl - Compile your Julia Package