nvim-treehopper
playground
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nvim-treehopper | playground | |
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6 | 16 | |
397 | 11,674 | |
- | 1.1% | |
0.0 | 0.0 | |
3 months ago | 3 months ago | |
Lua | TypeScript | |
GNU General Public License v3.0 only | Apache License 2.0 |
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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.
nvim-treehopper
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Differences in setting keymaps in lua vs map command
I am trying to set up nvim-treehopper and duplicating the suggested keymaps in a lua config.
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Neovim quick way to indent multiple lines
Take a look at nvim-treehopper (https://github.com/mfussenegger/nvim-treehopper), a tree-sitter based plugin that lets you select regions of code
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Leaps and (no) bounds - extend leap.nvim with custom motions, callbacks, Tree-sitter, and more
This copies the idea of nvim-treehopper. Just a work in progress hack (pretty usable though), but planning to make a full-fledged plugin, if someone else won't do it :) (Needless to say, my gists can be considered unlicensed, do whatever you want with them.)
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The most amazing built-in feature nobody ever mentions: smart select!
I also suggest taking a look at this one : mfussenegger/nvim-treehopper
- Bram: "Neovim has included Treesitter, which is an implementation of this. Once Vim9 is done I'll have a look at whether it is a good choice to include with Vim"
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Plugin: treesitter-unit
https://github.com/mfussenegger/nvim-ts-hint-textobject does what you are proposing.
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.
What are some alternatives?
spellsitter.nvim - Treesitter powered spellchecker
clip-interrogator - Image to prompt with BLIP and CLIP
nvim-treesitter-context - Show code context
dspy - DSPy: The framework for programming—not prompting—foundation models
NvChad - An attempt to make neovim cli as functional as an IDE while being very beautiful , blazing fast. [Moved to: https://github.com/NvChad/NvChad]
nvim-treesitter - Nvim Treesitter configurations and abstraction layer
zephyr-nvim - A dark neovim colorscheme written in lua
pyllama - LLaMA: Open and Efficient Foundation Language Models
nvim-gps - Simple statusline component that shows what scope you are working inside
lake.nvim - A simplified ocean color scheme with treesitter support
twilight.nvim - 🌅 Twilight is a Lua plugin for Neovim 0.5 that dims inactive portions of the code you're editing using TreeSitter.
developer - the first library to let you embed a developer agent in your own app!