I get 3fps on my chrome, most likely due to disabled HW acceleration
tpdly 2 hours ago [-]
Lovely visualization. I like the very concrete depiction of middle layers "recognizing features", that make the whole machine feel more plausible. I'm also a fan of visualizing things, but I think its important to appreciate that some things (like 10,000 dimension vector as the input, or even a 100 dimension vector as an output) can't be concretely visualized, and you have to develop intuitions in more roundabout ways.
I hope make more of these, I'd love to see a transformer presented more clearly.
If you want to understand neural networks, keep going.
ge96 2 hours ago [-]
I like the style of the site it has a "vintage" look
Don't think it's moire effect but yeah looking at the pattern
cwt137 1 hours ago [-]
This visualizations reminds me of the 3blue1brown videos.
giancarlostoro 1 hours ago [-]
I was thinking the same thing. Its at least the same description.
4fterd4rk 4 hours ago [-]
Great explanation, but the last question is quite simple. You determine the weights via brute force. Simply running a large amount of data where you have the input as well as the correct output (handwriting to text in this case).
ggambetta 3 hours ago [-]
"Brute force" would be trying random weights and keeping the best performing model. Backpropagation is compute-intensive but I wouldn't call it "brute force".
Ygg2 2 hours ago [-]
"Brute force" here is about the amount of data you're ingesting. It's no Alpha Zero, that will learn from scratch.
pks016 42 minutes ago [-]
Great visualization!
anon291 26 minutes ago [-]
Nice visuals, but misses the mark. Neural networks transform vector spaces, and collect points into bins. This visualization shows the structure of the computation. This is akin to displaying a Matrix vector multiplication in Wx + b notation, except W,x,and b have more exciting displays.
It completely misses the mark on what it means to 'weight' (linearly transform), bias (affine transform) and then non-linearly transform (i.e, 'collect') points into bins
javaskrrt 2 hours ago [-]
very cool stuff
Rendered at 19:19:17 GMT+0000 (Coordinated Universal Time) with Vercel.
I hope make more of these, I'd love to see a transformer presented more clearly.
If you want to understand neural networks, keep going.
Don't think it's moire effect but yeah looking at the pattern
It completely misses the mark on what it means to 'weight' (linearly transform), bias (affine transform) and then non-linearly transform (i.e, 'collect') points into bins