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Towards Data Science 02/17/2020 13:12
On regulation of algorithms. And I don’t mean in the obvious way that the adjective “artificial” leads to philosophical thought and debate about the essence of intelligence and therefore the essence of human nature. No news there. Just ask , or any philosopher of the mind. I mean that AI made us think again about the ethics and politics of computerized systems . Recently, I have noticed several voices call to “regulate algorithms”. Taken at face value, this phrase makes no sense. An algorithm is a recipe : a sequence of steps to solve a class of problems in a deterministic way. Surely, there is no need to regulate any algorithm in this sense. What the call for regulation actually intends to target is not what is classically known as an algor.
Towards Data Science 02/17/2020 09:41
Photo by: (via ). is an open-source DataFrame library for Python with an API that closely resembles that of . I have been using Vaex for several years in both academic and industry environments, and it is my go-to library for several of the data science projects I am working on. In this article I would like to share some of my favourite Vaex features. Some may be obvious by now, but some may surprise you. The following code examples are run on a MacBook Pro (15", 2018, 2.6GHz Intel Core i7, 32GB RAM). This article can also be. 1. Easy to work with very large datasets. Nowadays, it becomes increasingly more common to encounter datasets that are larger than the available RAM on a typical laptop or a desktop workstation. solves this problem rathe.

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