Unlock the secrets of protein folding and revolutionize how we approach drug design, disease understanding, and biological research. AlphaFold is transforming the field, and you can be a part of this revolution. We break down the complexities of AlphaFold into manageable steps. From the basics of tensors and machine learning to advanced geometry, we'll guide you through hands-on tutorials and interactive explanations. Become a pioneer in AI-driven structural biology. Gain the skills to not only use existing tools but to innovate and push the boundaries of what's possible.
I really enjoy this Tutorial. I am looking forward to see the AlphaFold specific lessons and really learn how the mystery of AlphaFold unravels. And thanks for the recommendation of other good tutorials on machine learning ❤
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!
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)
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!
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!
