Deep playground VS Distill

Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good. For example, take the circle dataset here: https://playground.tensorflow.org That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by... - Source: Hacker News / about 2 months ago

For visualisation and some fun: http://playground.tensorflow.org/. - Source: dev.to / 4 months ago

Https://seeing-theory.brown.edu/ https://www.3blue1brown.com/ https://playground.tensorflow.org/. - Source: Hacker News / 8 months ago

There’s an interactive neural network you can train here, which can give some intuition on wider vs larger networks: https://mlu-explain.github.io/neural-networks/ See also here: http://playground.tensorflow.org/. - Source: Hacker News / 10 months ago

This site is worth playing around with to get a feel for neural networks, and somewhat about ML in general. There are lots of strategies for statistical learning, and neural nets are only one of them, but they essentially always boil down into figuring out how to build a “classifier”, to try to classify data points into whatever category they best belong in. Source: 10 months ago

Distill was a new take at publishing research/ideas in deep learning in a visual way: https://distill.pub/ I love their articles and while it was hard to sustain, the quality of the ones in their are pretty good. They provide some tips and templates on how to develop such visual storytelling articles. - Source: Hacker News / 7 months ago

Explainable AI is far from early stages. Read into anthropic ai’s work in mechanistic interpretability like toy models of superposition along with the rest of the transformer-circuits papers. Read chris olah’s distill papers. Read neel nanda’s recent work on reverse engineering how language models grok modular addition. Read kevin meng’s work on locating and editing facts inside of gpt. Read openai’s paper on... Source: 11 months ago

I also wasn't aware of either The Pudding or distill.pub. So thanks for just mentioning those. Source: about 1 year ago

Anything from Setosa [0] is really good. It contains interactive, animated illustrations of several Machine Learning ideas. I _loved_ reading papers from Distill Pub [1] as they contained interactive diagrams. My most favorite one so far is the thread on Differentiable Self-organizing Systems [2]. I liked the lizard example very much as it is interactive, and lizards grow lost organs back. I think this is funny.... - Source: Hacker News / over 1 year ago

If you include deep learning in CS then https://distill.pub/ has a lot to offer in this category. - Source: Hacker News / over 1 year ago