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The Case for Learned Index Structures, Part 2: Hash Maps and Bloom Filters

The Case for Learned Index Structures, Part 2: Hash Maps and Bloom Filters

FromLinear Digressions


The Case for Learned Index Structures, Part 2: Hash Maps and Bloom Filters

FromLinear Digressions

ratings:
Length:
21 minutes
Released:
Jan 29, 2018
Format:
Podcast episode

Description

Last week we started the story of how you could use a machine learning model in place of a data structure, and this week we wrap up with an exploration of Bloom Filters and Hash Maps. Just like last week, when we covered B-trees, we'll walk through both the "classic" implementation of these data structures and how a machine learning model could create the same functionality.
Released:
Jan 29, 2018
Format:
Podcast episode

Titles in the series (100)

Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.