AlphaFold Decoded: Introduction to Tensors (Lesson 1)

[@AlphaFoldDecoded]

Hey everyone, at 13:21 the denominators for the second and third row should be e^3+e^4+e^5 and e^6+e^7+e^8 respectively, instead of e^0+e^1+e^2. Thanks for letting me know in the comments!

[@Flo52525]

I’ve been waiting for a tutorial like this for so long! Can’t wait to learn more about AlphaFold!!

[@AlphaFoldDecoded]

I am super glad that this tutorial was helpful for you. Please stayed tuned for the upcoming videos and let us know what you think!

[@xeniar9756]

Great job!! I could follow along really well so far and I‘m already looking forward to the next episode!

[@AlphaFoldDecoded]

Your support is amazing, thank you. I hope you enjoy what's next!

[@cariyaputta]

Can't wait for more lessons.

[@AlphaFoldDecoded]

Really appreciate your kind words!

[@maxwellm1365]

Thanks for explaining it so well :) So excited to learn more in the next video!!

[@AlphaFoldDecoded]

Thanks so much for watching and your support!

[@douglasespindola5185]

Wow! I saw this video and instantly subscribed! That is great value content. Thanks for sharing. Greetings from Brazil!

[@AlphaFoldDecoded]

I really appreciate your kind comment! More videos are on the way. Greetings back from Germany 👋

[@lukasothman1709]

A very helpful tutorial, thank you very much

[@AlphaFoldDecoded]

Thanks for you kind words!

[@adammajmudar889]

Please finish this series - looking forward to the whole thing. Breaking down AlphaFold is going to have huge value. I have one suggestion. While you can, start off the series differently with the high-level on AlphaFold first/videos on that and a more first principled breakdown of what we're building/high-level understanding before going into the code. Right now, the video about tensors without context on much else is less interesting. Especially because things that lack context don't tap into the right motivation circuits to make people interested, whereas if you explain first, then talk about the topics, it will be far more engaging. Good luck with this channel!

[@AlphaFoldDecoded]

Thank you so much for your feedback and encouragement! I really appreciate your suggestion and completely agree. Starting with a high-level overview of AlphaFold before diving into the code would definitely make the series more engaging and informative. I as well have found that providing context first helps people stay motivated through the detailed parts. The video on tensors was indeed a bit heavy on syntax, which can be less interesting without a bigger picture. I'll definitely incorporate a more principled breakdown in future videos. While I'll certainly try to put everything into a bigger picture in the context of AlphaFold, the first three videos I have planned will serve as a general introduction to machine learning concepts. These will touch on AlphaFold but primarily use more classic examples like images and NLP to explain the mechanisms, as these topics are often easier to interpret and will set a strong foundation. Thank you again for the tip, and I'm excited to continue this journey with you all. Stay tuned!

[@Jonas-pi3dw]

What a great and concise tutorial, will definitely follow along once I have time😊

[@AlphaFoldDecoded]

Thanks a lot; can't wait for you to see the next video!

[@JeomonGeorge]

Keep up the good work

[@rizbaruah752]

this is wonderful. thank you brother.

[@franziskax8586]

This is amazing!

[@AlphaFoldDecoded]

Your feedback is appreciated; exciting videos coming up!

[@zandacr0ss86]

nice and simple explanation

[@AlphaFoldDecoded]

Amazing, thank you for your nice feedback! I look forward to your opinion on the next chapters.

[@linuschka4015]

Wow thank you so much for this tutorial

[@AlphaFoldDecoded]

Thanks for the comment and for watching!

[@lialexwei509]

Wonderful project. Wondering if you're going to Cover AF3?

[@AlphaFoldDecoded]

I'd love too, all molecules models are so interesting. But I won't be able to before they do code release because I can't check my modules, and it will be more difficult because they don't do PyTorch. This series worked so well because OpenFold was already a PyTorch implementation that I could use to compare the results. But fingers crossed 🤞🏻

[@jimmys3575]

13:25 I could be mistaken here, but are the denominators in the second and third rows wrong? shouldn't it be e^3+e^4+e^5 and e^6+e^7+e^8 respectively instead of e^1+e^2+e^3, assuming you are doing Softmax(dim=1)

[@AlphaFoldDecoded]

You're absolutely right, thanks for pointing that out! I'll start a pinned comment for errors :)