Julia Computing is pleased to announce the launch of JuliaAcademy, the new Julia Computing training platform for 3 types of learning: self-directed, online instructor-led and in-person onsite training.
JuliaAcademy courses include: Intro to Julia, Machine Learning and Artificial Intelligence in Julia, Parallel Computing in Julia, Deep Learning with Flux, Optimization with JuMP and Machine Learning with Knet.
Self-directed training - all online, learn at your own pace
Instructor-led online training - live two-day courses taught by Julia Computing instructors
In-person training - contact us at [email protected] to schedule customized in-person training for your organization
Register now for instructor-led online courses:
Introduction to Julia
Introduction to Machine Learning and Artificial Intelligence in Julia
Parallel Computing in Julia
JuliaTeam: JuliaTeam is an enterprise solution that makes it as easy to use and develop Julia packages inside your company as it is in the open source world. The current release of JuliaTeam integrates with your corporate authentication systems and eliminates the headaches of installing and using public Julia packages behind a corporate firewall. JuliaTeam also gives IT and management insight and control over what packages developers are using, helping ensure quality and security.
Upcoming JuliaTeam releases will include these features as well:
Read and search docs for all internal and external packages in a single place
Create and manage private package registries
Publish and test private packages as easily as public ones, making sure new versions work seamlessly with all the other versions of packages that your teams are using
Benchmark your code to make sure it runs as efficiently as possible and stays fast
Download a summary of licenses of all the software you depend on
JuliaTeam makes Julia development at your company as easy, effective and fun as open source.
For more information, contact us.
Julia Ranks #4 Top Machine Learning Language on GitHub: GitHub reports that Julia ranks #4 on the list of the top machine learning projects by contribution and #6 on the list of top machine learning languages on GitHub.
Julia for Medicine: MIT News reports that the Julia Lab at MIT is working with the University of Maryland School of Pharmacy and other partners to speed drug approval using patient health data and sophisticated data analysis.
Julia and Julia Computing in the News
PLOS: “[I]t is encouraging to see several contributions describing open-source tools in this field, including a comprehensive package in Julia”
ZDNet: “Other big movers include … MIT-created up-and-comer Julia, which was up from 47th last January to 37th today.”
SD Times: “Other interesting positive moves of 2018 [include] … Julia (#47 to #37)”
House of Bots: “Julia programming language is fastest growing … charts rapid rise in 2018-19”
SIAM News: “Jeff Bezanson, Stefan Karpinski, and Viral B. Shah of Julia Computing are the 2019 recipients of the James H. Wilkinson Prize for Numerical Software.”
Heise: Julia-Sprachschöpfer Erhalten James H. Wilkinson Prize for Numerical Software
HPC Wire: “Julia can offer high performance at scale using hundreds of thousands of processor cores for compute.”
MIT News: “Julia Lab joins team to speed up drug approval process”
Outsourcing Pharma: “The Health Analytics Collective will be led by the Julia Lab”
GitHub: “Julia, R, and Scala all appear in the top 10 for machine learni)g projects”
HackerRank 2019 Developer Skills Report: 11.5% of developers want to learn Julia in 2019
ZDNet: “Julia … made its public debut in 2012 and over the past year has quickly climbed the ranks of the world’s most popular languages.”
JAXenter: “TensorFlow, Python, and Julia helped make 2018 the year of machine learning on GitHub”
DevClass: “Julia devs keep up momentum with 1.1 release”
SDTimes: “Julia 1.1 has been officially released”
ZDNet: “Downloads of Julia have grown 78% since January 2018, from 1.8 million to 3.2 million downloads”
DevClass: “Julia, R, and Scala all appear in the top 10 for machine learning projects”
JAXenter: “Solve differential equations with new Julia library”
Solutions Review: The Julia data ecosystem lets you load multidimensional datasets, perform aggregations, joins and preprocessing operations in parallel, and save them to a disk. Julia has foreign function interfaces for C/Fortran, C++, Python, R, and Java, and it can be embedded into other programs through an embedding API. The language works with an array of databases and integrates with the Hadoop ecosystem as well.”
Heise: “Julia 1.1 ist fertiggestellt”
Devellopez: “Le langage de programmation Julia gagne de plus en plus en popularité au sein de la communauté scientifique”
Analytics Insight: “Julia combines the functionality from different well-known languages like Python, R, Matlab, SAS and Stata with the speed of C++ and Java.”
InsideHPC: “One of my favorites is Julia language. It has a wonderful interface to GPUs via the JuliaGPU project. Unlike Python, it doesn’t have a GIL, as it is compiled and built for parallelism, nor does it have structure by indentation. It has a native GPU compiler built in to its LLVM stack, meaning that it can optimize not merely for the CPUs, but also for the GPUs. It is rapidly maturing, so there are a few rough spots, but I expect tools like this to become more standard for HPC applications.”
VentureBeat: Top machine learning projects on GitHub: #4 Julia
iProgrammer: Data science with Julia
TechNotification: Top Five New Programming Languages to Learn in 2019
FierceBiotech: “MMS teams with MIT’s Julia Lab and University of Maryland to create Health Analytics Collective”
TechRepublic: “Highly rated machine-learning repositories include Flux.jl, Knet.jl and Metalhead.jl”
House of Bots: “Highly rated machine-learning repositories include MachineLearning.jl, MLKernels.jl and LightML.jl”
Julia Blog Posts
Julia Ranking Trend, TIOBE, RedMonk (Liye Zhang)
DiffEqFlux.jl – A Julia Library for Neural Differential Equations (Chris Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit)
GeoStats Tutorials (Júlio Hoffimann)
Soss.jl: Design Plans for Spring 2019 (Chad Scherrer)
Kaggle: Prime Traveling Santa 2018-MIP (Ole Kröger)
Meta-Learning with Julia & Flux (Dominique Luna)
Think Julia: How to Think Like a Computer Scientist (Ben Lauwens, Allen Downey)
[Learning Julia with a Beginner](https://www.linkedin.com/pulse/learning-julia-beginner-week-1-ritwik-bandyopadhyay/ (Ritwik Bandyopadhyay)
Upcoming Julia Events
Online: Introduction to Machine Learning and Artificial Intelligence in Julia with Julia Computing Feb 4-5
Ghent: Intro to Julia with TensorFlow Belgium Feb 6
Online: Live YouTube Tutorial on DynamicalSystems.jl with George Datseris Feb 13
Spokane: Society for Industrial and Applied Mathematics (SIAM) Conference on Computational Science and Engineering (CSE19) with Jeff Bezanson, Stefan Karpinski and Viral Shah (Julia Computing) Feb 25-Mar 1
Online: Introduction to Machine Learning and Artificial Intelligence in Julia with Julia Computing Feb 25-26
Santiago: Third Annual JuMP-dev Workshop Mar 12-14
Baltimore: JuliaCon 2019 July 22-26
Recent Julia Events
Minneapolis/St. Paul: Julia Data Analysis with League of Extraordinary Algorithms at Veritas Technologies Jan 12
Taipei: MLDM Monday x Julia - Julia Tutorial IV with Taiwan R User Group Jan 14
Seattle: The Julia Programming Language with SeaLang Jan 16
Warsaw: Sekrety Systemu Typów w Języku Julia with Bogumił Kamiński and Warszawskie Forum Julia Jan 23
Cambridge, MA: Julia Guest Lectures with Jameson Nash, Keno Fischer, Jeff Bezanson and Viral Shah (Julia Computing) and Cambridge Area Julia Users Network (CAJUN) at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) Jan 24
Sydney: Intro to Julia Lang (Designed for AI & ML) + Hands on Coding! with Sydney Alt.Net User Group Jan 29
Budapest: Graph Processing in Julia with Gergely Hanczár Jan 30
Julia Jobs, Fellowships and Internships
Penn State University’s Department of Astronomy and Astrophysics Center for Exoplanets and Habitable Worlds (CEHW) is looking to hire a Postdoc Researcher and Assistant Research Professor with Julia experience
Do you work at or know of an organization looking to hire Julia programmers as staff, research fellows or interns? Would your employer be interested in hiring interns to work on open source packages that are useful to their business? Help us connect members of our community to great opportunities by sending us an email, and we’ll get the word out.
There are more than 300 Julia jobs currently listed on Indeed.com, including jobs at Accenture, Airbus, Amazon, AstraZeneca, Barnes & Noble, BlackRock, Capital One, Charles River Analytics, Citigroup, Comcast, Cooper Tire & Rubber, Disney, Facebook, Gallup, Genentech, General Electric, Google, Huawei, Johnson & Johnson, Match, McKinsey, NBCUniversal, Nielsen, OKCupid, Oracle, Pandora, Peapod, Pfizer, Raytheon, Zillow, Brown, Emory, Harvard, Johns Hopkins, Massachusetts General Hospital, Penn State, UC Davis, University of Chicago, University of Virginia, Argonne National Laboratory, Lawrence Berkeley National Laboratory, Los Alamos National Laboratory, National Renewable Energy Laboratory, Oak Ridge National Laboratory, State of Wisconsin and many more.
Contact Us: Please contact us if you wish to:
Purchase or obtain license information for Julia products such as JuliaAcademy, JuliaTeam, JuliaPro or JuliaBox
Obtain pricing for Julia consulting projects for your organization
Schedule Julia training for your organization
Share information about exciting new Julia case studies or use cases
Spread the word about an upcoming conference, workshop, training, hackathon, meetup, talk or presentation involving Julia
Partner with Julia Computing to organize a Julia meetup, conference, workshop, training, hackathon, talk or presentation involving Julia
Submit a Julia internship, fellowship or job posting
About Julia and Julia Computing
Julia is the fastest high performance open source computing language for data, analytics, algorithmic trading, machine learning, artificial intelligence, and other scientific and numeric computing applications. Julia solves the two language problem by combining the ease of use of Python and R with the speed of C++. Julia provides parallel computing capabilities out of the box and unlimited scalability with minimal effort. Julia has been downloaded more than 3 million times and is used at more than 1,500 universities. Julia co-creators are the winners of the 2019 James H. Wilkinson Prize for Numerical Software. Julia has run at petascale on 650,000 cores with 1.3 million threads to analyze over 56 terabytes of data using Cori, one of the ten largest and most powerful supercomputers in the world.
Julia Computing was founded in 2015 by all the creators of Julia to develop products and provide professional services to businesses and researchers using Julia.