Julia is a high-level dynamic programming language designed to address the needs of high-performance numerical analysis and computational science. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library.
Live code in Pandoc Markdown
Making your benchmark of optimization algorithms simple and open
Julia syntax highlighting for Sublime Text
A Python library for numpy arrays that persist on disk in a format that is simple, self-documented and tool-independent, and maximizes universal readability.
Data science analyses delving into National Hockey League (NHL) ice hockey statistics
Chaque mois, quelques liens et nouveautés sur le langage Julia
Memory efficient convolution networks
AHPCC Workshop - Getting Started with AHPCC
Fractals with zoom functionality in Python and Julia
News Article Clustering Using Unsupervised Machine Learning Algorithms
This research work is about Limited Data Acquisition for the real life physical experiment of fluid flow across cylinder based on Kernelized Extended Dynamic Mode Decomposition by incorporating Gaussian Random Matrix Theory and Laplacian Kernel Function Hilbert space.
Repository for study of machine learning and data science algorithms.
Quantum Optimal Control via Semi-Automatic Differentiation
Pyjulia is a python module for calling julia functions inside your python code.
an app to let you program at a Julia REPL with your voice.
Functions in R, Julia, and Python to fit a set of LOESS smoothers to the shot length data of a motion picture in order to identify the temporal structure of a film's editing without committing the analyst to a particular level of smoothing before applying the function.
Created by Jeff Bezanson, Stefan Karpinski, Viral B. Shah, Alan Edelman
Released February 14, 2012