- 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.
by helloplanets
0 subcomment
- For the visual learners, here's a classic intro to how LLMs work: https://bbycroft.net/llm
- 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.
- The original Show HN, https://news.ycombinator.com/item?id=44633725
by 8cvor6j844qw_d6
1 subcomments
- 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?
- I love this visual article as well:
https://mlu-explain.github.io/neural-networks/
- 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
by jetfire_1711
0 subcomment
- 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.
- This visualizations reminds me of the 3blue1brown videos.
by 4fterd4rk
1 subcomments
- 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).
by artemonster
1 subcomments
- I get 3fps on my chrome, most likely due to disabled HW acceleration
- 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
- Great visualization!
- very cool stuff
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