[HN Gopher] Understanding Neural Network, Visually
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Understanding Neural Network, Visually
Author : surprisetalk
Score : 192 points
Date : 2026-02-03 14:49 UTC (3 days ago)
(HTM) web link (visualrambling.space)
(TXT) w3m dump (visualrambling.space)
| brudgers wrote:
| The original Show HN,
| https://news.ycombinator.com/item?id=44633725
| 4fterd4rk wrote:
| 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 wrote:
| "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 wrote:
| "Brute force" here is about the amount of data you're
| ingesting. It's no Alpha Zero, that will learn from scratch.
| jazzpush2 wrote:
| 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.
| esafak wrote:
| 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.
| helloplanets wrote:
| For the visual learners, here's a classic intro to how LLMs work:
| https://bbycroft.net/llm
| tpdly wrote:
| 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.
| ge96 wrote:
| 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
| Bengalilol wrote:
| Lucky you!
|
| <https://visualrambling.space/dithering-part-1/>
|
| <https://visualrambling.space/dithering-part-2/>
| ge96 wrote:
| Oh god my eyes! As it zooms in (ha)
|
| That's cool, rendering shades in the old days
|
| Man those graphics are so good damn
| javaskrrt wrote:
| very cool stuff
| cwt137 wrote:
| This visualizations reminds me of the 3blue1brown videos.
| giancarlostoro wrote:
| I was thinking the same thing. Its at least the same
| description.
| pks016 wrote:
| Great visualization!
| anon291 wrote:
| 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 wrote:
| > 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.
| artemonster wrote:
| I get 3fps on my chrome, most likely due to disabled HW
| acceleration
| nerdsniper wrote:
| High FPS on Safari M2 MBP.
| 8cvor6j844qw_d6 wrote:
| 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?
| adammarples wrote:
| Yes, vanilla neural networks are just lots of perceptrons
| jazzpush2 wrote:
| I love this visual article as well:
|
| https://mlu-explain.github.io/neural-networks/
| jetfire_1711 wrote:
| 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.
| shrekmas wrote:
| As someone who does not use Twitter, I suggest adding RSS to your
| site.
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