Python Julia Resources

Advanced Scientific Computing: producing better code This course is taught as a 6-session "nanocourse" at Washington University in St. Louis. See the

Course Description The programming language Julia is being more and more adopted in High Performance Computing (HPC) due to its unique way to combine

Deep Learning with Julia DL with Julia is a book about how to do various deep learning tasks using the Julia programming language and specifically the

Julia package for contraction of tensor networks, based on the sweep line algorithm outlined in the paper General tensor network decoding of 2D Pauli codes

Wordle.jl A Julia library for solving Wordle puzzles. Usage julia import Wordle: play julia play("panic") 4 julia play("panic", verbose = true) I

Julia and Mandelbrot Picture Maker This simple functions create simple plots of the Julia and Mandelbrot sets. The Julia set require the important par

Knet Knet (pronounced "kay-net") is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU

PySDM PySDM is a package for simulating the dynamics of population of particles. It is intended to serve as a building block for simulation systems mo

Iris Species Predictor Iris prediction is used to classify iris species using their sepal length, sepal width, petal length and petal width created us

X4DS This repo is a collection of Python & Julia port of codes in the following excellent R books: An Introduction to Statistical Learning (ISLR) Stat

Julia-Mandelbrot-s-Fractals A little software to generate and save Julia or Mandelbrot's Fractals. Dependencies : Python 3.7 or more. (Also possible t

PythonCall & JuliaCall Bringing Python® and Julia together in seamless harmony: Call Python code from Julia and Julia code from Python via a symmetric

PlutoTest.jl (alpha release) Visual, reactive testing library for Julia A macro @test that you can use to verify your code's correctness. But instead

Have you always wished Jupyter notebooks were plain text documents? Wished you could edit them in your favorite IDE? And get clear and meaningful diff

TensorGraphicalModels TensorGraphicalModels.jl is a suite of Julia tools for estimating high-dimensional multiway (tensor-variate) covariance and inve

PyMPDATA PyMPDATA is a high-performance Numba-accelerated Pythonic implementation of the MPDATA algorithm of Smolarkiewicz et al. used in geophysical

papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. Papermill lets you: parameterize notebooks execute notebooks This

FastAI Docs: Stable | Dev FastAI.jl is inspired by fastai, and is a repository of best practices for deep learning in Julia. Its goal is to easily ena

MacroTools.jl MacroTools provides a library of tools for working with Julia code and expressions. This includes a powerful template-matching system an

LowRankModels.jl LowRankModels.jl is a Julia package for modeling and fitting generalized low rank models (GLRMs). GLRMs model a data array by a low r

Calling Julia from Python - an experiment on data loading See the slides. TLDR After reading Patrick's blog post, we decided to try to replace C++ wit

Substrate_Mediated_Invasion Julia and Matlab codes to simulated all problems in El-Hachem, McCue and Simpson (2021) 2DSolver.jl reproduces the simulat

Book on Julia for Data Science

Perspective: Julia for Biologists 1. Examples Speed: Example 1 - Single cell data and network inference Domain: Single cell data Methodology: Network

Parallelized symbolic regression built on Julia, and interfaced by Python. Uses regularized evolution, simulated annealing, and gradient-free optimization.

Apache MXNet (incubating) for Deep Learning Master Docs License Apache MXNet (incubating) is a deep learning framework designed for both efficiency an

logo CoCalc Collaborative Calculation and Data Science CoCalc is a virtual online workspace for calculations, research, collaboration and authoring do

Dash Dash is the most downloaded, trusted Python framework for building ML & data science web apps. Built on top of Plotly.js, React and Flask, Dash t

Dash Dash is the most downloaded, trusted Python framework for building ML & data science web apps. Built on top of Plotly.js, React and Flask, Dash t

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

Dash Dash is the most downloaded, trusted Python framework for building ML & data science web apps. Built on top of Plotly.js, React and Flask, Dash t

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m