nearley
micrograd
nearley | micrograd | |
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3 | 22 | |
3,560 | 8,581 | |
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0.0 | 0.0 | |
9 months ago | 25 days ago | |
JavaScript | Jupyter Notebook | |
MIT License | MIT License |
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nearley
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Writing a C compiler in 500 lines of Python
While I suspect I would learn more writing a tokenizer and parsing logic myself I find grammars much easier to read and maintain.
ANTLR is pretty good and is supported across several languages and something I had previously used for some quick Elasticsearch query syntax munging in Python. It also means you can often start from an already existing grammar.
The JS version of ANTLR didn't seem to work for me so for the SQL/JSONPath stuff ended up using the Moo lever and Nearly parser which was rather pleasant. https://nearley.js.org
- Parser generators vs. handwritten parsers: surveying major languages in 2021
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Applicative Parsing
Parsers in nearley.js [1] are written in a very readable EBNF-like DSL; then they get desugared down to a JS file that's a lot like your snippet.
[1] https://github.com/kach/nearley
micrograd
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Micrograd-CUDA: adapting Karpathy's tiny autodiff engine for GPU acceleration
I recently decided to turbo-teach myself basic cuda with a proper project. I really enjoyed Karpathy’s micrograd (https://github.com/karpathy/micrograd), so I extended it with cuda kernels and 2D tensor logic. It’s a bit longer than the original project, but it’s still very readable for anyone wanting to quickly learn about gpu acceleration in practice.
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Stuff we figured out about AI in 2023
FOr inference, less than 1KLOC of pure, dependency-free C is enough (if you include the tokenizer and command line parsing)[1]. This was a non-obvious fact for me, in principle, you could run a modern LLM 20 years ago with just 1000 lines of code, assuming you're fine with things potentially taking days to run of course.
Training wouldn't be that much harder, Micrograd[2] is 200LOC of pure Python, 1000 lines would probably be enough for training an (extremely slow) LLM. By "extremely slow", I mean that a training run that normally takes hours could probably take dozens of years, but the results would, in principle, be the same.
If you were writing in C instead of Python and used something like Llama CPP's optimization tricks, you could probably get somewhat acceptable training performance in 2 or 3 KLOC. You'd still be off by one or two orders of magnitude when compared to a GPU cluster, but a lot better than naive, loopy Python.
[1] https://github.com/karpathy/llama2.c
[2] https://github.com/karpathy/micrograd
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Writing a C compiler in 500 lines of Python
Perhaps they were thinking of https://github.com/karpathy/micrograd
- Linear Algebra for Programmers
- Understanding Automatic Differentiation in 30 lines of Python
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Newbie question: Is there overloading of Haskell function signature?
I was (for fun) trying to recreate micrograd in Haskell. The ideia is simple:
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[D] Backpropagation is not just the chain-rule, then what is it?
Check out this repo I found a few years back when I was looking into understanding pytorch better. It's basically a super tiny autodiff library that only works on scalars. The whole repo is under 200 lines of code, so you can pull up pycharm or whatever and step through the code and see how it all comes together. Or... you know. Just read it, it's not super complicated.
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Neural Networks: Zero to Hero
I'm doing an ML apprenticeship [1] these weeks and Karpathy's videos are part of it. We've been deep down into them. I found them excellent. All concepts he illustrates are crystal clear in his mind (even though they are complicated concepts themselves) and that shows in his explanations.
Also, the way he builds up everything is magnificent. Starting from basic python classes, to derivatives and gradient descent, to micrograd [2] and then from a bigram counting model [3] to makemore [4] and nanoGPT [5]
[1]: https://www.foundersandcoders.com/ml
[2]: https://github.com/karpathy/micrograd
[3]: https://github.com/karpathy/randomfun/blob/master/lectures/m...
[4]: https://github.com/karpathy/makemore
[5]: https://github.com/karpathy/nanoGPT
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Rustygrad - A tiny Autograd engine inspired by micrograd
Just published my first crate, rustygrad, a Rust implementation of Andrej Karpathy's micrograd!
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Hey Rustaceans! Got a question? Ask here (10/2023)!
I've been trying to reimplement Karpathy's micrograd library in rust as a fun side project.
What are some alternatives?
PEG.js - PEG.js: Parser generator for JavaScript
deepnet - Educational deep learning library in plain Numpy.
Jison - Bison in JavaScript.
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
Chevrotain - Parser Building Toolkit for JavaScript
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
markdown-it - Markdown parser, done right. 100% CommonMark support, extensions, syntax plugins & high speed
ML-From-Scratch - Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
xml2js - XML to JavaScript object converter.
NNfSiX - Neural Networks from Scratch in various programming languages
parse5 - HTML parsing/serialization toolset for Node.js. WHATWG HTML Living Standard (aka HTML5)-compliant.
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors