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Understanding Neural Network, Visually (visualrambling.space)
artemonster 3 minutes ago [-]
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.

helloplanets 2 hours ago [-]
For the visual learners, here's a classic intro to how LLMs work: https://bbycroft.net/llm
brudgers 2 days ago [-]
esafak 3 hours ago [-]
This is just scratching the surface -- where neural networks were thirty years ago: https://en.wikipedia.org/wiki/MNIST_database

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
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