playground
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playground | neovim | |
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16 | 1,383 | |
11,662 | 76,256 | |
1.0% | 2.4% | |
0.0 | 10.0 | |
3 months ago | 2 days ago | |
TypeScript | Vim Script | |
Apache License 2.0 | GNU General Public License v3.0 or later |
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
- Visualization of Common Algorithms
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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:
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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.
neovim
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Let's See Your Terminal
This got me thinking about my recent pivot, my switch to Neovim by way of LazyVim to write most of my code, and using tmux to keep terminal states alive after closing a session.
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Level Up Your Dev Workflow: Conquer Web Development with a Blazing Fast Neovim Setup (Part 1)
Neovim: Make sure you have Neovim installed on your system. You can check the official website for installation instructions: https://neovim.io/ Git: We'll be using Git to clone the LazyVim starter pack. If you don't have Git, you can download it from https://git-scm.com/downloads
- Helix - Front-End Power
- Neovim
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Effective Neovim Setup. A Beginner’s Guide
There are several ways to install Neovim. This wiki provides several guidelines on how to install Neovim.
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Aftermath of switching from VSCode to Neovim
All these thoughts I've shared, I would have them on occasion - but ever since I switched to Linux and Neovim, my curiosity has been through the roof. Switching over to Neovim and Linux was a not so fun weekend of configuration and spending half a day getting my work's local dev environment running on my new OS (which no one has tested development on). But I now have a deeper understanding of the tools I use, and have a text editor configured to be the most optimal for the way I want to use it.
- Neovim is 10 years old today
- Neovide – a simple, no-nonsense, cross-platform GUI for Neovim
- Neovim v0.9.5 Released
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Clipboards, Terminals, and Linux
I've recently switched to Neovim, and with it begun using the terminal mouse support. But, this has the side-effect that I can't just click-and-drag to select text in the terminal anymore -- Neovim controls that as well.
What are some alternatives?
clip-interrogator - Image to prompt with BLIP and CLIP
vim9 - An experimental fork of Vim, exploring ways to make Vim script faster and better.
nvim-treesitter - Nvim Treesitter configurations and abstraction layer
helix - A post-modern modal text editor.
dspy - DSPy: The framework for programming—not prompting—foundation models
neovide - No Nonsense Neovim Client in Rust
pyllama - LLaMA: Open and Efficient Foundation Language Models
doom-emacs - An Emacs framework for the stubborn martian hacker [Moved to: https://github.com/doomemacs/doomemacs]
lake.nvim - A simplified ocean color scheme with treesitter support
AstroVim - AstroNvim is an aesthetic and feature-rich neovim config that is extensible and easy to use with a great set of plugins [Moved to: https://github.com/AstroNvim/AstroNvim]
developer - the first library to let you embed a developer agent in your own app!
LunarVim - 🌙 LunarVim is an IDE layer for Neovim. Completely free and community driven.