DataScience
Julia-on-Colab
| DataScience | Julia-on-Colab | |
|---|---|---|
| 9 | 1 | |
| 478 | 159 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| about 1 year ago | almost 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
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:
Julia-on-Colab
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Cheap online compute for ML projects in Julia?
This is the correct answer. This is the link: https://github.com/Dsantra92/Julia-on-Colab
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.
dl-colab-notebooks - Try out deep learning models online on Google Colab
julia_titanic_model - Titanic machine learning model and web service
machine-learning-asset-management - Machine Learning in Asset Management (by @firmai)
DataFrames.jl - In-memory tabular data in Julia
ThreeBodyBot - Poorly written code that generates moderately exciting plots of a very specific physics phenomenon that enthralls dozens of us around the globe.
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
interesting-colabs - Personal colab collections which I feel interesting.
PlotDocs.jl - Documentation for Plots.jl
simfin-tutorials - Tutorials for SimFin - Simple financial data for Python