MIT Lectures
Added 2020-09-04 17:10:42 +0000 UTCHi Everyone,
I mentioned in a previous post that I'd be collaborating with an MIT group on a course they're running about "computational thinking" this semester. Well, it's happening! Linked above is the first live lecture I gave. There was also an asynchronous lecture earlier in the week giving the basics of working with images in the language used for this class (Julia).
Course intro: https://youtu.be/vxjRWtWoD_w
Lecture 1: https://youtu.be/8rrHTtUzyZA
It's a funny little elective course, where the plan is, admittedly, rather scattered. There are four units, image processing, ray tracing, epidemic modeling, and applications to climate science. Each one is meant to give a context where we can talk about some general principles in computation and math. Also, given that the group running the course is MIT's Julia lab, it's effectively a course on working with Julia as much as anything else.
My own role here is simply to give the occasional lecture and generally provide advising on the nature of teaching over the internet. So far, it's been a wonderful chance to learn new things and play around with different styles, e.g. putting the code much more front-and-center than it typically is in a lot of my other work.
It's not entirely decided how frequently I'll lecture. The general goal is to be helpful to the course, but without interfering too much with keeping things moving on the 3blue1brown front.
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Also, the Hamming code videos are now out, including Ben Eater's, which you should most certainly check out.
Ben Eater's video:
https://youtu.be/h0jloehRKas
Part 1:
https://youtu.be/X8jsijhllIA
Part 2:
https://youtu.be/b3NxrZOu_CE
I hope you're all doing well,
-Grant
Comments
There I was playing around in Figma
3blue1brown
2021-04-16 00:18:56 +0000 UTCCan you share what tools you used for the convolution stepping visualization and the polynomial multiplication part?
John Nichols
2021-04-10 20:24:27 +0000 UTCI don't do a ton of Julia programming, but you're completely right that automatic differentiation would make a good topic, and that this language makes it particularly natural.
3blue1brown
2020-09-06 21:28:27 +0000 UTCEnjoyable and clearly explained. I am looking forward to more.
white beard geek
2020-09-05 15:26:59 +0000 UTCI hope the notebook will be shared so that we can run the examples and extend them
2020-09-05 00:02:41 +0000 UTCThe pacing might need to be different for a lecture versus a video. I notice my own kids play most content at 2X speed.
2020-09-04 20:09:19 +0000 UTCMy daughter is named Julia so I approve.
2020-09-04 20:04:41 +0000 UTCThis is great. I'm going to try to take the course like a real student.
Doug Fort
2020-09-04 19:01:34 +0000 UTCThank you for this, I'm excited to check these out!
2020-09-04 18:28:14 +0000 UTCGreat, clear, and filled with new learnings that I always enjoy in your videos!
2020-09-04 18:24:54 +0000 UTCAs someone who needs this kind of understanding "only sometimes" as a professional (my last linear algebra or calculus class was over 25 years ago), this was a really helpful reminder of what convolving really means intuitively. But I think your comment is really right: 30 minutes is too fast and clipped. Your style could easily spread out over 45 minutes without dragging. I wish you could have just a little more time to not feel rushed.
2020-09-04 18:07:03 +0000 UTCHi Grant, do you also do a lot of programming in Julia? One of the things I liked most about the language (apart from the dot and nerdy things like metaprogramming) is the libraries for automatic differentiation, especially Zygote. It's amazing to me that you can just write an complex algorithm as you would program it and then differentiate it. Maybe the principles behind this would be a cool video? I read some stuff from Zygote about adjoints and vector-Jacobian products but even though I know quite a bit of calculus it was all very unfamiliar to me ;)
2020-09-04 17:57:04 +0000 UTCLooked good!
Gabe
2020-09-04 17:39:23 +0000 UTC
