Understanding Bias in Machine Learning
June 24th, 2020
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The standard way of training and deploying Machine Learning (ML) models is with centralized architectures. This means that there are centralized servers, that receive requests from devices and respond with ML predictions. These systems have the disadvantange, as with any other centralized system, that they can be overloaded and that they must have central access to all data. In this course we show how distributed ML systems can be trained and deployed in a way that eliminates some of the problems of centralized architectures.
In this free and short course we show how to build a crawler that detects broken links in a website. We designed it to help us find broken links in our own DELTA LAB website, and figured other people will benefit from it as well, so we decided to make into a small free course. The task at hand is to go through every link in a website and keep track of those that come back as errors when visited, while keeping control of not going into external sites in a way that we end up crawling the whole internet.