julia-tsp
DataScience
| julia-tsp | DataScience | |
|---|---|---|
| 2 | 9 | |
| 19 | 478 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| almost 2 years ago | about 1 year 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.
julia-tsp
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
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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:
What are some alternatives?
Knet.jl - Koç University deep learning framework.
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-on-Colab - Notebook for running Julia on Google Colab
julia_titanic_model - Titanic machine learning model and web service
DataFrames.jl - In-memory tabular data in Julia
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
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
seaborn - Statistical data visualization in Python
scikit-learn - scikit-learn: machine learning in Python
NumPy - The fundamental package for scientific computing with Python.
JLD2.jl - HDF5-compatible file format in pure Julia