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Newsletter May 2019 - Upgraded Julia Package Documentation and Search Powered by JuliaTeam

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Q: Is there an easy-to-find, easy-to-use searchable website where I can find a comprehensive inventory of Julia packages and Julia package documentation?

A: Pkg.julialang.org now includes improved Julia package documentation and search powered by JuliaTeam. You can now search Julia package names, tags, code and documentation to find the best Julia packages that fit your requirements.

Please contact us to learn how JuliaTeam can provide this same functionality within your enterprise development environment, including your own private Julia packages and more.

Stefan Karpinski explains in JuliaTeam Vision:

Documentation. Providing a single place to find all documentation for the Julia packages that you use. This service offers a single consistent place and way to host and publish package documentation. It also makes cross-linking docs between packages easy since they all live in the same place. Developers shouldn't ever have to set up or think about the how of documentation hosting‚ they should just need to follow standard conventions for inline docs and then push their code. The docs service does the rest: cross-linked, searchable (see the next bullet point) docs are generated automatically.

Search. Currently search and discovery of packages is a serious pain point in the Julia ecosystem. JuliaTeam will provide integrated search of documentation and code for all packages. This will let you find the package that does what you need, whether it's a public open source package or a private package that your organization uses‚ they'll all be searchable in a single place.

Los Alamos National Laboratory Uses Julia to Predict Power Outages Caused by Extreme Events: Los Alamos National Laboratory used Julia to develop free, open source package - PowerModelsMLD.jl - that simulates the impact of disasters and predicts how the electric grid will be affected. This software can be used to allocate evacuation, rescue, relief and recovery resources.

 

Naval Postgraduate School Researchers Use Julia for Next Generation Climate Model: Naval Postgraduate School Professors Frank Giraldo, Lucas Wilcox and Jeremy Kozdon use Julia to create a new Earth Systems Model that is "poised to be the most accurate climate modeling system to date.‚"

Julia: The Programming Language Machine Learning Needs

The discussion around the future of machine learning continues to be atopic of interest at conferences and on Twitter. As workloads become diverse and complex, and generalize from the neural networks of today to Differentiable Programming, the question about programmability naturally arises. We have published several blogs on the topic (What is Differentiable Programming and Reinforcement Learning vs. Differentiable Programming).

Facebook AI's Soumith Chintala has this to say about Julia:

JuliaAcademy: Julia Computing's training offerings continue to expand. JuliaAcademy is the Julia Computing training platform for 3 types of learning: self-directed, online instructor-led and in-person onsite training.

Course Title and Description Date (11 am - 3 pm EDT) Cost Register
***Introduction to Machine Learning and Artificial Intelligence in Julia*** May 2-3 $500 Register
***Parallel Computing in Julia*** May 8-9 $500 Register


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.

JuliaAcademy provides:

  1. Self-directed training - all online, learn at your own pace

  2. Instructor-led online training - live two-day courses taught by Julia Computing instructors

  3. In-person training - contact us at info@juliacomputing.com to schedule customized in-person training for your organization

Register now for instructor-led online courses. All courses include 8 hours of instruction: 4 hours per day for two consecutive days. Currently scheduled courses are from 11 am - 3 pm Eastern Daylight Time (US).

Julia & Flux - Modernizing Machine Learning: Computação Brasil published Julia e Flux: Modernizando o Aprendizado de Máquina by Dhairya Gandhi, Mike Innes, Elliot Saba, Keno Fischer and Viral Shah.

Algorithms for Optimization (Using Julia): Mykel Kochenderfer and Tim Wheeler have published Algorithms for Optimization which uses Julia to provide a comprehensive introduction to optimization with a focus on practical algorithms.

Julia Programming for Operations Research: Changhyun Kwon from the University of South Florida has published Julia Programming for Operations Research.

JuliaCon 2019: JuliaCon 2019 will be held July 22-26 at the University of Maryland, Baltimore. Early Bird Ticket Sales end May 5.

JuliaCon is looking for sponsors and university partners in diversity. Sponsorship is available at several levels and benefits include prominent mention and logo placement at JuliaCon and in JuliaCon conference materials and Website, an opportunity to present to JuliaCon participants, presentation space during the conference and registration for JuliaCon attendees. Past JuliaCon sponsors include the Alfred P. Sloan Foundation, Microsoft, Maven, Invenia, Julia Computing, Capital One, Gordon and Betty Moore Foundation, Gambit Research, Tangent Works, Amazon, Alan Turing Institute, Jeffrey Sarnoff, EVN and Conning.

Julia and Julia Computing in the News

  • Analytics India: 10 Fastest Growing Programming Languages That Employers Demand In 2019

  • Analytics India: Annual Survey On Data Science Recruitment In India: 2019

  • Apple: What Are the Biggest Software Challenges in Machine Learning?

  • Computação Brasil: Julia e Flux: Modernizando o Aprendizado de Máquina

  • Computing: The Top 10 Most In-Demand IT Skills for 2019

  • DevClass: Julia 101 - The Upstart Language with a Lot to Offer

  • Edgy: Why Julia is the Programming Language set to Dominate our Future

  • EFinancialCareers: Should You Learn to Program in Julia to Get Ahead in Finance?

  • Forbes: How Are Computer Programming Languages Created?

  • Forbes: What Will Machine Learning Look Like In Twenty Years?

  • HPCWire: Julia and NASA Help the Nature Conservancy Save the Planet with Circuitscape

  • LeiPhone: 芯片行业30年资深人士:AI为何是高性能计算史上 "最大的变革推动者"

  • MoneyControl: Algo Trading: Here Are Five Steps to Set Up Your Own Algorithm

  • ODSC: Reinforcement Learning vs. Differentiable Programming

  • PC Revue: Päť Predikcií: Takto Bude Vyzerať Strojové Učenie o 20 Rokov

  • Sohu:十大应用在数学的计算机语言

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