you know what Zese! jan Misali is back and so is Jack Eisenmann! can you believe how long it took me to make this? I sure can’t! sorry!
http://patreon.com/hbmmaster

Being right all the time isn’t necessarily the best idea. This talk examines how to count distinct items from a firehose of data, how to determine if we’ve seen a given item before, and why absolute accuracy may be impractical when doing so.

Probabilistic data structures trade accuracy for approximate results, speed and economy of resources. They provide fast, scalable solutions to problems such as counting likes on social media posts, or determining which articles on a website a user has previously read.

I’ll introduce the Hyperloglog and Bloom Filter, explain how they work at a high level, and demonstrate different ways in which each can be leveraged in Python.

A GitHub repo to accompany this talk can be found at https://github.com/simonprickett/python-probabilistic-data-structures

Slides: https://simonprickett.dev/no_maybe_and_close_enough_slides.pdf

This video was recorded during the live online event zkSessions: The ZK Languages Rundown on May 19 2021.

Title: Programmable Privacy with Noir
Speaker: Zac Williamson (Aztec)

This event was produced by the Zero Knowledge Podcast.

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BIOL 6301 – Sp21 – Statistical Analysis of Ecological Communities