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Closures are one of the most powerful tools in JavaScript; they can help you write faster, cleaner, and safer code. No matter if you are using AngularJS, EmberJS, JQuery or another framework, closures are an invaluable tool you should be taking advantage of. Lets review the how and why of closures, and give you some examples to play with on your own. 
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isuAlgo is an ongoing project, and more complex visualisations are still being developed. To try this online quiz feature, click the "Start training!" button on the top right corner of this page.
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I love machine learning algorithms. I’ve taught classes and seminars and given talks on ML. The subject is fascinating to me, but like all skills fascination simply isn’t enough. To get good at something, you need to practice! Through this series of articles, I’ll teach you the fundamental machine learning algorithms using Javascript — not Python or Octave — as the example language. Originally I intended to write these articles in a variety of languages (PHP, JS, Perl, C, Ruby), but decided to stick with Javascript for the following reasons:
While I’ll be writing these articles with Javascript in mind, please re-write the examples in your language of choice as homework! Practice is how you get better, and writing the same algorithm several times in different languages really helps you understand the paradigms better.
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Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records; we built it to power analytics for Square Register, allowing merchants to slice and dice their payment history fluidly. Since most interactions only involve a single dimension, and then only small adjustments are made to the filter values, incremental filtering and reducing is significantly faster than starting from scratch. Crossfilter uses sorted indexes (and a few bit-twiddling hacks) to make this possible, dramatically increasing the performance of live histograms and top-K lists. For more details on how Crossfilter works, see the API reference.
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I think things can be simplified to a single abstraction since the underlying problem to all of this is the same. I present to you CSP and the concept of channels. CSP has been highly influential in Go and recently Clojure embraced it as well with core.async. There's even a C version. It's safe to say that it's becoming quite popular (and validated) and I think we need to try it out in JavaScript. I'm not going to spend time comparing it with every other solution (promises, FRP) because it would take too long and only incite remarks about how I wasn't using it right. I hope my examples do a good enough job convincing you themselves.
Jan Hesse's insight:
Communicating sequential processes
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The pressure is on to harness machine learning for more responsive enterprise apps. So find the open source tool that works best for your programming style and architecture.
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I recently released a fun project called rainyday.js. I consider it a rather humble piece of code and, in fact, it is more or less my first contact with JavaScript on a level that requires a little more than displaying alert popups. Hopefully, some of you will find it useful and interesting. The idea behind rainyday.js is to create a light JavaScript library that makes use of the HTML5 canvas to render an animation of raindrops falling on a glass surface. Simple enough, yet challenging at times, especially since we’re trying to avoid that cartoonish look usually attributed to JavaScript animations and at the same time make sure the animations work smoothly. In this article I’ll try to describe the overall approach as well as the “little things”, so bare with me. The animation uses three different canvas objects in order to achieve the layering effect, as can be seen on the figure below...
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