Recommender systems or Collaborative filtering is the process of filtering for information using techniques involving collaboration among multiple agents. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data
Here is the list of python libraries for building recommender systems.
Crab is a ﬂexible, fast recommender engine for Python that integrates classic information ﬁltering recom- mendation algorithms in the world of scientiﬁc Python packages (numpy, scipy, matplotlib). The engine aims to provide a rich set of components from which you can construct a customized recommender system from a set of algorithms. The…
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Love the physical buttons. Reminds me of the starting scene of War Games… https://youtu.be/rLMCjuge6oE
TL;DR: Thanks to an Arduino, an Arduino ethernet shield, some Arduino code, buttons from eBay, LEDs from Fry’s, and SmugMug’s deployment web app, we’ve created a push-button deployment process that looks like this:
When I first started working at SmugMug, we deployed infrequently by manually merging branches, tagging, double-checking, then running a bunch of commands (some via sudo and some not). It was an eleven step process that not all developers had access to. Developers were usually uneasy about pushing due to the complexity involved.
Eventually the process was consolidated into a shell script, which still had to be run via sudo on a designated server. More recently, the shell script was wrapped in a web app that made things much easier.
While the web app is pretty awesome and easy to use, I thought using a real physical button to deploy code would be even better:
Introducing the SmugMug…
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