cranium for Node.js – Pincer.io

The amount of data you need to build a good classifier increases with the number of features you have, so out of memory errors become a problem when dealing with thousands of features. For example, Weka fails to perform logistic regression with more than a couple thousand features on a 5mb dataset. Cranium never assumes that your instances can fit in memory, so you can use it on terabytes of data.

Cranium works with node streams, so you have a lot of flexibility with your input. Using streams sacrifies speed for memory efficiency — Cranium uses a constant amount of memory that is typically below 100mb. The speed penalty is significant: Cranium runs about 500x slower than LearnKit. If your dataset can fit in memory, Cranium is probably not right for you.


Schedule.js

A flexible scheduler for tasks, work items, reservations, meetings, etc.
The easiest way to schedule work items with complex dependencies across developers that all have different work schedules. Or reserve elevators in an apartment building. Or schedule the company ping pong tournament. Works in Node and in the browser.

I was getting lots of errors in my Express-based REST API due to invalid Json payloads being posted to my endpoint.
Express just throws an exception, but it doesn’t give you the raw payload to see what is causing the error.
I dug into the source code of the body parser and it revealed this simple trick to get the raw payload in req.rawBody:

<br /> app.use(express.bodyParser({<br /> verify: function(req, res, buf, encoding) {<br /> req.rawBody = buf.toString(encoding);<br /> }<br /> }));<br />


bell.js

Bell.js is a real-time anomalies(outliers) detection system for periodic time series, built to be able to monitor a large quantity of metrics. It collects metrics form clients like statsd, analyzes them with the 3-sigma, once enough anomalies were found in a short time it alerts us via sms/hipchat etc.


Using Machine Learning and NodeJS to detect the gender of Instagram Users | TOTEMS

The goal of this article is to provide a very practical guide to deploying a machine learning solution at scale. Not everything is proven right or optimal, and as with any real-life deployment, we made some trade-offs and took some shortcuts on the go without necessarily building all the evidence that would have been required in an academic setting.

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