BeautifulAlgorithms.jl
ReinforcementLearningAnIntroduction.jl
| BeautifulAlgorithms.jl | ReinforcementLearningAnIntroduction.jl | |
|---|---|---|
| 3 | 1 | |
| 1,308 | 299 | |
| - | 1.3% | |
| 4.1 | 0.0 | |
| 5 months ago | about 1 month ago | |
| Julia | Julia | |
| - | MIT License |
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BeautifulAlgorithms.jl
- Concise and beautiful algorithms written in Julia
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I’m trying to find a short document I can share to convince my team of scientific C++ programmers to give Julia a try. Any suggestions?
https://github.com/mossr/BeautifulAlgorithms.jl might convince them
ReinforcementLearningAnIntroduction.jl
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Multi-armed bandits & Clojure: An interactive exploration of a classic reinforcement learning problem using Clojure
Thanks. I'll take a look. Another interesting repo that takes a crack at this is https://github.com/JuliaReinforcementLearning/ReinforcementLearningAnIntroduction.jl. This is a Julia language implementation of the exercises in the book.
What are some alternatives?
ScikitLearn.jl - Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/
scittle - Execute Clojure(Script) directly from browser script tags via SCI
Coluna.jl - Branch-and-Price-and-Cut in Julia
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
RegressionTables.jl - Journal-style regression tables
Matches.jl - Simple ML package that computes gradients without backpropagation
EvoTrees.jl - Boosted trees in Julia
FluxTraining.jl - A flexible neural net training library inspired by fast.ai
JuliaComputation - Repository for Common Ground C25
StatsWithJuliaBook
ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations