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this week's favorite
A map for becoming a great data engineer.
This article reviews some common options for parallelizing Python code, including process-based parallelism, specialized libraries, ipython parallel, and Ray.
In the first post, I present some common techniques for graph analysis that should help us better understand our data.
If you have a dataset which is divided into categories of data like: kickstarter projects, flower species or most popular car brands, then it's a good idea to visualize those data categories to see the amount of values within each category.
In this work, we investigate the properties of RL agents that treat their observations as an arbitrarily ordered, variable-length list of sensory inputs. Here, we partition the visual input from CarRacing (Left) and Atari Pong (right) into a 2D grid of small patches, and shuffled their ordering. Each sensory neuron in the system receives a stream of visual input at a particular permuted patch location, and through coordination, must complete the task at hand, even if the visual ordering is randomly permuted again several times during an episode.
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