playground
telescope.nvim
Our great sponsors
playground | telescope.nvim | |
---|---|---|
16 | 322 | |
11,674 | 13,961 | |
1.1% | 5.7% | |
0.0 | 9.1 | |
3 months ago | 2 days ago | |
TypeScript | Lua | |
Apache License 2.0 | MIT License |
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
-
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.
-
Introduction to TensorFlow for Deep Learning
For visualisation and some fun: http://playground.tensorflow.org/
- TensorFlow Playground – Tinker with a NN in the Browser
-
Visualization of Common Algorithms
https://seeing-theory.brown.edu/
https://www.3blue1brown.com/
https://playground.tensorflow.org/
-
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/
-
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.
-
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
-
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.
-
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.
-
[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.
telescope.nvim
-
Level Up Your Dev Workflow: Conquer Web Development with a Blazing Fast Neovim Setup (Part 1)
for telescope.nvim (optional) live grep: ripgrep find files: fd
-
Neovim: creating keymaps in lua
Here we have a configuration for telescope.nvim, a very popular fuzzy finder.
- What is the reason people 'touch' a file before writing it?
-
What are the plugins/settings to be able to view individual file or folder contents while scrolling through files or folders?
EDIT: I found what I was looking for https://github.com/nvim-telescope/telescope.nvim and https://github.com/nvim-telescope/telescope-file-browser.nvim
-
What are some plugins that you can't live without?
Fuzzy Finder: fzf.vim (for its speed) along with telescope.nvim (for its ecosystem)
-
Telescope.nvim: Fully Customizable Layout!
Just landed on Telescope.nvim: https://github.com/nvim-telescope/telescope.nvim/pull/2572
-
telescope-sg: a new way to do structural search in neovim
This extension allows you to use the power of ast-grep to find code patterns in your editor, using the familiar and awesome interface of telescope.nvim.
- Telescope.nvim: Find, Filter, Preview, Pick. All Lua, All the Time
-
Benchmarking some of my favourite neovim plugins over time
telescope.nvim
-
Why does vim.lsp.buf.definition open this window instead of taking me to the styles file (the same with tsserver and Volar)?
My solution is using telescope.nvim with lsp extension, and map the vim.lsp.buf.definition keybinding to telescope one https://github.com/nvim-telescope/telescope.nvim
What are some alternatives?
clip-interrogator - Image to prompt with BLIP and CLIP
fzf.vim - fzf :heart: vim
dspy - DSPy: The framework for programming—not prompting—foundation models
fzf-lua - Improved fzf.vim written in lua
nvim-treesitter - Nvim Treesitter configurations and abstraction layer
vim-fugitive - fugitive.vim: A Git wrapper so awesome, it should be illegal
pyllama - LLaMA: Open and Efficient Foundation Language Models
telescope-fzf-native.nvim - FZF sorter for telescope written in c
lake.nvim - A simplified ocean color scheme with treesitter support
Visual Studio Code - Visual Studio Code
developer - the first library to let you embed a developer agent in your own app!
nvim-tree.lua - A file explorer tree for neovim written in lua