DataScience
JLD2.jl
| DataScience | JLD2.jl | |
|---|---|---|
| 9 | 2 | |
| 478 | 527 | |
| 0.0% | 2.1% | |
| 0.0 | 8.1 | |
| about 1 year ago | 9 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
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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
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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:
JLD2.jl
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
First, you need to save the model from the notebook to a file. For this you can use JLD2.jl module. This module used to serialize Julia object to HDF5-compatible format (which is well known by Python data scientists) and save it to a file.
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Best format to save matrices to a text file? (R interop)
I didn't realize this was the Julia subreddit. HDF5 or multiple CSV files would be my suggestion. As a side note, check out the JLD2 package. It's a HDF5 compatible format where the package is written in pure Julia.
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.
julia - The Julia Programming Language
Julia-on-Colab - Notebook for running Julia on Google Colab
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
julia_titanic_model - Titanic machine learning model and web service
Plots.jl - Powerful convenience for Julia visualizations and data analysis
DataFrames.jl - In-memory tabular data in Julia
Rocket.jl - Functional reactive programming extensions library for Julia
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
StatsWithJuliaBook
ThreeBodyBot - Poorly written code that generates moderately exciting plots of a very specific physics phenomenon that enthralls dozens of us around the globe.
PlotDocs.jl - Documentation for Plots.jl