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Finding the label errors with Cleanlab with Curtis Northcutt - 006

Finding the label errors with Cleanlab with Curtis Northcutt - 006

FromMachine Learning Cafe


Finding the label errors with Cleanlab with Curtis Northcutt - 006

FromMachine Learning Cafe

ratings:
Length:
56 minutes
Released:
Jan 23, 2020
Format:
Podcast episode

Description

In this episode, I talked with Curtis Northcutt about his application cleanlab, with which you can find label errors in your dataset. Cleanlab computes cross-validated probabilities, the confident joint, and the statistics used in uncertainty estimation for dataset labels, and it ranks and sorts the labels by the probabilities of error, so you can easily find them in your dataset. Curtis' website: https://www.curtisnorthcutt.com/ Curtis on LinkedIn: https://www.linkedin.com/in/cgnorthcutt/ Cleanlab on GitHub: https://github.com/cgnorthcutt/cleanlab Cleanlab's blog: https://l7.curtisnorthcutt.com/cleanlab-python-package White Papers: https://arxiv.org/abs/1911.00068 https://arxiv.org/abs/1705.01936 Music by Curtis (PomDP the PhD rapper): https://soundcloud.com/thephdrapper/bars-on-bars https://soundcloud.com/thephdrapper/crown https://soundcloud.com/thephdrapper/dub-dub https://open.spotify.com/album/2Fjg3zF8PGEg9WWNoeyx3X ---General Info--- Podcast's Website: http://machinelearningcafe.org/ Host's LinkedIn: https://www.linkedin.com/in/miklostoth/ Email of the host: miklos@machinelearningcafe.org ---Copyright Info--- Music is from https://filmmusic.io, intro first part is by Miklos Toth and some free garage band loops. :) intro second part: "Aces High" by Kevin MacLeod, outro: "Bars on Bars" by Curtis Northcutt (with his explicit allowance to play)
Released:
Jan 23, 2020
Format:
Podcast episode

Titles in the series (16)

This is a machine-learning-focused Podcast, where we interview people in the field of Artificial Intelligence and discuss interesting technical topics of Machine Learning. In the episodes, we focus on business-related use-cases (especially with Deep Learning ) and we also try to bring some technical white papers to the ground, not forgetting on the way that there are always some people behind the technology, so we try to understand their motivation and drive.