playground
nvim-ts-rainbow
playground | nvim-ts-rainbow | |
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16 | 21 | |
11,690 | 865 | |
0.8% | - | |
0.0 | 8.3 | |
3 months ago | over 1 year ago | |
TypeScript | Lua | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
playground
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Why do tree-based models still outperform deep learning on tabular data? (2022)
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 radius would do the trick. Now try doing that in 1000 dimensions. Sometimes you can, sometimes you can't or do want to bother.
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Introduction to TensorFlow for Deep Learning
For visualisation and some fun: http://playground.tensorflow.org/
- TensorFlow Playground – Tinker with a NN in the Browser
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Visualization of Common Algorithms
https://seeing-theory.brown.edu/
https://www.3blue1brown.com/
https://playground.tensorflow.org/
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Stanford A.I. Courses
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/
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Let's revolutionize the CPU together!
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.
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Curious about Inputs for neural network
I don’t know much experimenting you’ve done, but many repeated small scale experiments might give you a better intuition at least. I highly recommend this online tool for playing with different environmental variables, even if you’re comfortable coding up your own experiments: http://playground.tensorflow.org
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Intel Announces Aurora genAI, Generative AI Model With 1 Trillion Parameters
Even if you can’t code, play around with this tool: https://playground.tensorflow.org — you can adjust the shape of the NN and watch how well it classifies the data. Model size obviously matters.
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Where have all the hackers gone?
I don't think so. You can easily play around in the browser, using Javascript, or on https://processing.org/, https://playground.tensorflow.org/, https://scratch.mit.edu/, etc.
If anything the problem is that today's kids have too many options. And sure, some are commercial.
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[Discussion] Questions about linear regression, polynomial features and multilayer NN.
Well there is no point of using a multilayer linear neural network, because a cascade of linear transformations can be reduced to a single linear transformation. So you can only approximate linear functions. However if you have prior knowledge about the non linearity of your data lets say you know that it is a linear combination of polynomials up to certain degree, you can expand your input space by explicitly making non linear transformation. For instance a 1D linear regression can be modeled by 2 input neurons and 1 output neuron where the activation of the output is the identity. The input neuron x0 will take a constant input namely 1 and the second input neuron x1 will takes your data x. The output neuron will be y=w_0 * 1+w_1 *x which is equal to y=w_0 +w_1 * x. Let us say that your data follows a polynomial form, the idea is to add input neurons and expand your input to for instance X=[1 x x2] in this case you have 3 input neurons where the third is an explict non linear form of the input so y=w_0 + w_1 x +w_2 x2. The general idea is to find a space where the problem becomes linear. In real life example these spaces are non trivial the power of neural network is that they can find by optimization such space without explicitly encoding these non linearities. Try playing around with https://playground.tensorflow.org/ you can get an intuition about your question.
nvim-ts-rainbow
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TS: Level of a node based on capture group?
for the past few days I have been working on a fork to the nvim-ts-rainbow plugin: nvim-ts-rainbow2. I am pretty much done, except for one small issue: finding out the level of a node relative to other container nodes. I know how to determine the level of a node in the tree (just keep counting up from 1 while going through the parents until I hit the root), but that is not what I need.
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nvim-ts-rainbow is archived and no longer maintained
I noticed that it was abandoned when I was about to update my PR. The PR as it is up there is a mess, so I went through a major refactor and subsequently lost everything like an idiot.
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How to configure nvim UI to look like this?
The "look" you're looking for is given by a bunch of plugins: - OneDark.nvim as colorscheme - TS Rainbow for rainbow brackets - BarBar for bufferline - Nvim Devicons and NerdFonts to view file icons - NvimTree as a file manager - Indent Blankline to show indentation guides - CompetiTest with vertical split UI - Feline as statusline plugin. In the screenshot feline is configured with a custom theme. As you can see statusline is different for CompetiTest buffers: a different statusline can be configured for every different filetype using conditional_config.
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Lua alternative to vim-matchup
For highlightning parentheses you could check out nvim-ts-rainbow
- Supercharge your Haskell experience in neovim
- Rainbow indent guides like vscode
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Is there any very colorful Onedark colorscheme for Neovim? Onedark.nvim and Onedarkpro.nvim are nice, but I still feel they are a little bit colorful compared to the syntax-highlight of this Onedark I used in VSCode.
Consider using nvim-ts-rainbow to get rainbow parentheses.
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Code highlighting sucks on Neovim.
To get changed colors for nested brackets, use nvim-ts-rainbow. I think the rest of the comments have you covered on getting colors up to snuff for you. To me it just looks like mismatched colors, not that anything is wrong
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nvim-ts-rainbow improved highlighting of JSX
I'm happy to say I have fixed [the bug in nvim-ts-rainbow] that caused all JSX props to be highlighted](https://github.com/p00f/nvim-ts-rainbow/issues/118) in extended_mode instead of just highlighting the tag names. It was bugging me for a while when working on React components. Now, only the tag names and angle brackets in JSX elements are highlighted.
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Lisp programming configuration for neovim
Lsp support is pretty good with Neovim, but obviously depends on what Lisp you use. I also like ts-rainbow a lot, but that's literally just visual fluff for brackets
What are some alternatives?
clip-interrogator - Image to prompt with BLIP and CLIP
Bracket-Pair-Colorizer-2 - Bracket Colorizer Extension for VSCode
dspy - DSPy: The framework for programming—not prompting—foundation models
indent-blankline.nvim - Indent guides for Neovim
nvim-treesitter - Nvim Treesitter configurations and abstraction layer
rainbow - Rainbow Parentheses Improved, shorter code, no level limit, smooth and fast, powerful configuration.
pyllama - LLaMA: Open and Efficient Foundation Language Models
rainbow_parentheses.vim - :rainbow: Simpler Rainbow Parentheses
lake.nvim - A simplified ocean color scheme with treesitter support
iceberg.vim - :antarctica: Bluish color scheme for Vim and Neovim
developer - the first library to let you embed a developer agent in your own app!
nvim-treesitter-refactor - Refactor module for nvim-treesitter