Zygote-Mutating-Arrays-WorkAround.jl
DataScience
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- | MIT License |
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Zygote-Mutating-Arrays-WorkAround.jl
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.
What are some alternatives?
JuliaTutorials - Learn Julia via interactive tutorials!
Unitful.jl - Physical quantities with arbitrary units
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
NonlinearSolve.jl - High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
Zygote-Mutating-Arrays-WorkArou
AeroSandbox - Aircraft design optimization made fast through modern automatic differentiation. Composable analysis tools for aerodynamics, propulsion, structures, trajectory design, and much more.
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
Julia-on-Colab - Notebook for running Julia on Google Colab
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
julia_titanic_model - Titanic machine learning model and web service
DataFrames.jl - In-memory tabular data in Julia