About the Part 1 (2020) category

Thank you @jeremy , looking forward to the advanced part :slight_smile:

This is great news! I’m very much looking forward to attending the course.

Good luck with finishing the course and getting the book out :smiley:

such an honor! Thanks! Jeremy!

Great.

The book signing should be Jeremy labelling the fish on the front cover as a tench!

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But it’s not a tench! :open_mouth:

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Thanks for the invite, looking forward to it. Just preordered the book :smiley:

Maybe it should be :wink:

I totally expected it to be a tench, too.

Thanks for taking the time to integrate the key info from Intro to ML into the course. There has been ongoing interest in that course in the forums and even some questions about it on the TWiML forum. Now that mlcourse.ai has become self-paced (no more live sessions), this is great timing!

I don’t know that one. Is it good?

Will there be an implementation of instance segmentation (i.e. Mask RCNN) in the new courses or is it planned to be added to the fastai library?
I am thinking of some sort of wrapper around detectron2 for example.

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But what if I told you it was a fish that’s distinctive for being deeper than it is long?

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Fantastic! thank you very much for the invitation and really looking forward to this :wink: (looking forward to the book as well :wink: )

smart! :face_with_monocle: I think it’s a John Dory, right? It’s a relatively common fish around here. Very tasty :yum: @brismith & @Jess, why did you expect the cover :fish: to be a tench?

Hmmm, no, on second thoughts it’s not a John Dory. It’s missing the characteristic spot on the side.

:100: It’s a good resource because it’s project based and the math is also covered for those who want to learn those details.

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In one of the lectures, Jeremy mentioned that the image he shows is a fish called a “tench”. One of the “tricks” (mnemonic) for remembering things is to associate an unusual visualization with what you’re trying to remember. There’s even an app for it: https://www.picmonic.com/ I think that it stood out and we remembered it because it was an unusual, visual bit of trivia.

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Then you’d need to be careful with your transforms!

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I think Jeremy also said that data scientist recognize tench as it is an imagenet category.

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