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.
Oh wow, this looks like a 3d render of a perceptron when I started reading about neural networks. I guess essentially neural networks are built based on that idea? Inputs > weight function to to adjust the final output to desired values?
mr_toad 7 hours ago [-]
The layers themselves are basically perceptrons, not really any different to a generalized linear model.
The ‘secret sauce’ in a deep network is the hidden layer with a non-linear activation function. Without that you could simplify all the layers to a linear model.
sva_ 8 hours ago [-]
A neural network is basically a multilayer perceptron
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 15 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 15 hours ago [-]
"Brute force" here is about the amount of data you're ingesting. It's no Alpha Zero, that will learn from scratch.
jazzpush2 12 hours ago [-]
What? Either option requires sufficient data. Brute force implies iterating over all combinations until you find the best weights. Back-prop is an optimization technique.
Ygg2 3 hours ago [-]
In context of grandparents post.
> 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
Brute force just means guessing all possible combinations. A dataset containing most human knowledge is about as brute force as you can get.
I'm fairly sure that Alpha Zero data is generated by Alpha Zero. But it's not an LLM.
jetfire_1711 11 hours ago [-]
Spent 10 minutes on the site and I think this is where I'll start my day from next week! I just love visual based learning.
cwt137 14 hours ago [-]
This visualizations reminds me of the 3blue1brown videos.
giancarlostoro 14 hours ago [-]
I was thinking the same thing. Its at least the same description.
11 hours ago [-]
shrekmas 9 hours ago [-]
As someone who does not use Twitter, I suggest adding RSS to your site.
atultw 5 hours ago [-]
Nice work
artemonster 12 hours ago [-]
I get 3fps on my chrome, most likely due to disabled HW acceleration
nerdsniper 12 hours ago [-]
High FPS on Safari M2 MBP.
anon291 13 hours 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
titzer 12 hours ago [-]
> but misses the mark
It doesn't match the pictures in your head, but it nevertheless does present a mental representation the author (and presumably some readers) find useful.
Instead of nitpicking, perhaps pointing to a better visualization (like maybe this video: https://www.youtube.com/watch?v=ChfEO8l-fas) could help others learn. Otherwise it's just frustrating to read comments like this.
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.
- make a visualization of the article above and it would be the biggest aha moment in tech
Don't think it's moire effect but yeah looking at the pattern
<https://visualrambling.space/dithering-part-1/>
<https://visualrambling.space/dithering-part-2/>
That's cool, rendering shades in the old days
Man those graphics are so good damn
The ‘secret sauce’ in a deep network is the hidden layer with a non-linear activation function. Without that you could simplify all the layers to a linear model.
https://en.wikipedia.org/wiki/Multilayer_perceptron
https://mlu-explain.github.io/neural-networks/
I'm fairly sure that Alpha Zero data is generated by Alpha Zero. But it's not an LLM.
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
It doesn't match the pictures in your head, but it nevertheless does present a mental representation the author (and presumably some readers) find useful.
Instead of nitpicking, perhaps pointing to a better visualization (like maybe this video: https://www.youtube.com/watch?v=ChfEO8l-fas) could help others learn. Otherwise it's just frustrating to read comments like this.