Boosting vs. semi-supervised learning

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While gradient boosted algorithms are amazing, they aren't a silver bullet for everything. Especially when you're dealing with a dataset that only has a very small set of labels. For those use-cases you may want to resort to semi-supervised learning techniques instead.

To learn more about label propagation, check the API docs here:

00:00 Describing the edge case
01:35 When classifiers fail
04:03 Semi supervised
09:42 Applied

This whiteboard video is part of the open efforts over at probabl. To learn more you can check out website or reach out to us on social media.

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Super clear + intuitive explanation on label propagation, huge thanks!!

FarizDarari

I just found out your channel a couple of weeks ago and I have to say it: your content is absolute gold. Thank you.

Moncamonca

That connection from KNNs to graphs and markov chains just blew my mind. It's so obvious in hindsight, but other people teaching markov chains don't bother with setting up the intuition.

ulamss

Big fan of your work man. Somehow, even after obtaining a bachelors and masters degree in AI, I learn many new things from your videos each time.

quinnscot

I will literally watch anything by Vincent Warmerdam

josephbolton

I don't expect that any human would find the structure at 4:03 (and for most other examples as well) without the grey dots. Great video tho!

thomasdeniffel

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