JuliaCon 2015
who it's for
Julia developers, data scientists, scientific programmers, high-performance computing engineers, machine learning engineers, visualization engineers, database engineers, quality assurance engineers
topics
multi-threading infrastructure in Julia; concurrent and parallel programming; large scale recommender systems; crunching big data with Julia; optimising organized transport; solving the two language problem; interactive visualizations and UIs; ThreeJS.jl interactive 3D graphics; working with different databases; fast database interfacing with MySQL.jl; why Julia is fast; quality assurance using Julia
| title | author | length |
|---|---|---|
| Solving the "Two Language" Problem in Data Science | Stefan Karpinski | 47m |
Claims concrete primitives that remove the language gap and shows implemented features that actually interoperate and run on real data: shelling out that… | ||
| Beating the averages at JustDial with Julia | Sandipan Chattopadhyay | 15m |
JustDial rewrites its pricing, budgeting and search pipelines in Julia, replacing a PHP plus C module stack with a single pricing engine that trains overnight… | ||
| Optimising organized transport at scale | Swaminathan Padmanabhan | 23m |
The objective of the talk is to introduce the audience to some of the problems that the Data sciences group @Ola works on. Ola being the largest organized taxi… | ||
| The Upcoming Multi-threading Infrastructure in Julia | Ranjan Anantharaman | 17m |
Claims a concrete multithreading implementation for Julia: a data-parallel for with broadcast and barrier synchronization, atomic support in the runtime, and… | ||
| Crunching Big Data with Julia | Tanmay K. Mohapatra | 20m |
Shows a working big data workflow built on Elly.jl and demonstrates that the client is a pure Julia HDFS and Yarn client with no libhdfs dependency. Implements… | ||
| Concurrent and Parallel programming in Julia | Amit Murthy | 22m |
This talk will give an introduction to the various
concurrent programming primitives: Tasks, async-wait, Channels.
parallel programming primitives: adding… | ||
| Large-Scale Parallel Recommender System in Julia | Abhijith Chandraprabhu | 17m |
A demonstration of a movie recommender system implemented end-to-end in Julia that uses matrix factorization (ALS or a similar algorithm). It reports dataset… | ||
| Interactive Visualizations and UIs in Julia | Shashi Gowda | 19m |
Shows concrete tooling: Gadfly and PyPlot for plotting, IJulia to run notebooks, Interact to add interactive sliders and widgets, and Escher to generate Escher… | ||
| ThreeJS.jl: Interactive 3D Graphics in the Browser using Julia | Rohit Thankachan | 14m |
ThreeJS.jl is a Julia package that wraps threejs for Julia and exposes scene, mesh, camera and material primitives so Julia code builds real WebGL scenes… | ||
| Working with many different databases in Julia | Pradeep | 07m |
This talk will touch upon the following:
1) Support for various databases
2) Feature-wise comparison
3) Performance of various db drivers
4) Some… | ||
| Fast database interfacing with MySQL.jl | Nishanth Kottary | 17m |
MySQL.jl is a julia interface to the MySQL C connector. Apart from wrappers around MySQL C functions, MySQL.jl provides high level features such as converting… | ||
| Quality Assurance using Julia | Harsha Byadarahalli Mahesh | 09m |
Uploader promises an introduction to Julia packages and systematic QA approaches but the captions are gibberish and offer no evidence of concrete results… | ||
| Why is Julia fast? | Jeff Bezanson | 30m |
Claim: Julia hits near‑C speeds by moving uncertainty into the type system and specializing code. The implementation uses multiple dispatch, aggressive type… | ||
| Machine Learning with Julia | Dinker Mattam | 21m |
Claims three years of exclusive Julia use and practical work on random forests and data curation, in particular time series data. Shows reading/writing CSV… | ||
| nothing matches | ||