In this series of posts, we’ll be featuring mathematical video and streaming channels from all over the internet, by speaking to the creators of the channel and asking them about what they do.
We spoke to Grant Sanderson, author of the 3Blue1Brown channel, which now has over 5 million subscribers and has been posting videos since about 2016.
Channel title: 3Blue1Brown
Topics covered: Mathematics through animations
Average video length: 10-30 minutes
Recommended videos: ‘But What is a Fourier Series?’, ‘But What is a Neural Network?’, ‘But Why is a Sphere’s Surface Area Four Times its Shadow?’
More info: 3blue1brown.com
What is your channel about, and when/why did it start?
3blue1brown is about visualizing mathematics. The specifics topics range from very pure fields, like number theory or topology, to more applied settings, such as neural networks or physics, but in all cases the general theme is to put the visuals first, and let the explanation form around that.
For me it started as a coding project, actually, when I wanted to play around making my own (very scrappy) math animation library many years ago. I made one video with it, then improved the tool a bit, then made another, and just kept going.
Who is the intended audience for the channel?
Many of the topics end up being around an early college level, but the hope is to find topics which are approachable to anyone with a high school level of familiarity with math, and still interesting even to the professionals in the field. I rarely succeed, of course, but that’s the aim.
What is a typical video like?
Videos tend to range between 10 and 30 minutes, focusing either on diving into a “what is ____” style question someone may have wondered before (“What is a neural network”, “what is a quaternion”, “what is the central limit theorem”, etc.), or otherwise introducing an intriguing puzzle and walking down a path for how you could find the solution to such a puzzle.
Why should people watch? Why is it different to other mathematical video content?
By putting the time into creating visualizations for each idea presented in the video, it’s often possible for relatively deep topics to still feel approachable, since sometimes the cognitive effort in understanding a mathematical argument is dominated by just unpacking what all the formulas are really saying.
What exciting plans do you have for the future?
Immediately on deck is a video about a physics demo which promises to be quite fun. After that, I may return to some topics in deep learning which I started a series on 5 years ago, but which could most certainly use some updating based on everything that has happened since then.