NeuronDotNet - Neural Networks in C# | Free Science & Engineering software downloads. Visualizing Data with AngularJS. The D3 bits but new donut charts will all have the same data... demo demo demo (aka, small multiples) demo but what if our data changes? There's nothing you can do. jk svg.on('mousedown', function(d) { data = d3.range(4).map(Math.random); arcs.data(pie(data)).attr('d', arc); }); demo demo Wait, WAT? Angular doesn't know the scope changed. how does it know?! Var $timeout = function(callback, delay){ setTimeout(function(){ $scope.apply(function(){ callback(); }); }, delay); } it usually knows automatically via: ng-mouseoverng-click$timeout$httpetc... svg.on('mousedown', function(d) { scope. demo sweet rrr.. wait a sec.. now ng-repeat don't work... demo demo awesome rrrr... wait.. what about animation?
Scope. transitioning arc angles demo whoops... lets try that again. transitioning arc angles demo okay, but things break if the length of our data doesn't remain the same. now what? Scope. demo But wait! You're right, it's not svg.on('mousedown', function(d) { scope. demo. MATLAB Plot Gallery - MathWorks United Kingdom. How a Math Genius Hacked OkCupid to Find True Love - Wired Science. Mathematician Chris McKinlay hacked OKCupid to find the girl of his dreams. Emily Shur Chris McKinlay was folded into a cramped fifth-floor cubicle in UCLA’s math sciences building, lit by a single bulb and the glow from his monitor. It was 3 in the morning, the optimal time to squeeze cycles out of the supercomputer in Colorado that he was using for his PhD dissertation.
(The subject: large-scale data processing and parallel numerical methods.) While the computer chugged, he clicked open a second window to check his OkCupid inbox. McKinlay, a lanky 35-year-old with tousled hair, was one of about 40 million Americans looking for romance through websites like Match.com, J-Date, and e-Harmony, and he’d been searching in vain since his last breakup nine months earlier. On that early morning in June 2012, his compiler crunching out machine code in one window, his forlorn dating profile sitting idle in the other, it dawned on him that he was doing it wrong. Maurico Alejo. Visualising Data.
Data Science.
Data Science BrightTalk. Prof. Mak Skilton, Warwick Business School Digitization of products and services is changing industry supply chains, markets and jobs. How business companies and the providers of IT services and systems respond is increasingly driven by what digital ecosystems and roles will you play? How do you plan and optimize your digital options? The explosion of data and the drive for a multiplicity of customer driven experience and choice is creating an increasingly price competitive digital footprint to be mindful of. What characteristics are important to establishing a competitive product and service in a digitalized marketplace and industry?
This session will explore these themes · Definitions of what are your digital ecosystems · How to design digital workspaces in your digital ecosystem · Managing change in your organization to align and exploit your digital ecosystem presence · What are the liminality of these digital ecosystem See Mark's forthcoming book – “Digital Ecosystems”.
Introduction to Computational Finance and Financial Econometrics By Eric Zivot (University of Washington) Learn mathematical, programming and statistical tools used in the real world analysis and modeling of financial data. Get an in-depth insight into the mathematical and statistical tools and techniques used in quantitative and computational finance! In this course, you'll make use of R to analyze financial data, estimate statistical models, and construct optimized portfolios.
You will learn how to build probability models for assets returns, the way you should apply statistical techniques to evaluate if asset returns are normally distributed, how to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and the usage of optimization methods to construct efficient portfolios. The material in this course was originally developed as a complement to Prof.
Eric Zivot's Coursera lectures. This course is for everyone interested in finance. Machine Learning - Gibbs Sampling Tutorial — i am trask. History and Tutorial of Gibbs Sampling - Markov Chain Monte Carlo This will be discussing Markov Chains, Monte Carlo Analysis, and how they are combined under an application known as Gibbs Sampling. The general purpose of the combination is to use the efficiency of Monte Carlo and the memoryless “random walk” of a Markov Chain to computationally estimate the value of an integral.
The need to efficiently integrate is a broad need across many fields of science, with this approach being quite effective in integrating over a probability distribution. This paper will be separating the parts of the algorithm in order to understand both the origins of each part, and how the unique properties of each part contributes to the overall nature of the algorithm. What is the history of the algorithm? (Who invented it, when, and why?) Monte Carlo Method Monte Carlo analysis has a very exciting history. Markov Chains Gibbs Sampling For what problems is the algorithm appropriate? What are its advantages?