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Submitted by Rudi MK (@rudimk) on Tuesday, 5 August 2014
- We’ll first start off with an introduction to why many of the current languages used for math and science, have issues - non-asynchronous natures, server-based, and so on.
- We’re going to move on to some really good and extensive libraries, on symbolic computation, statistics, machine and deep learning and set theory. We’ll talk about the problems they’re typically meant for and used in, and we’ll check out some basic examples.
- We’re now going to go deeper into the rabbit hole, and discuss how one can run mathematical models and expose them over APIs using Node and Express; how these APIs can then be consumed using d3.js for visualizing results; running mathematical models inside Google’s Native Client, or on embedded hardware using Cylon.js; oh, and we might throw in some WebGL for fun!
Pen and paper, if you like taking notes. Tablets work too.
I’m Rudraksh, and I specialize in computational math. I’ve got varied experience in using math and data science for journalism, events management as well as ed-tech and social media startups. Currently, I’m working on a startup called MathHarbor, where we’re building a cloud platform and hub for computational math and stats using open-source languages and toolsets. You can check it out here: http://mathharbor.com
Also, I’ve given a talk on tech and data journalism, at a Startup Saturday event in Delhi back in 2013. Check it out here: https://www.youtube.com/watch?v=peF47AwmLG4