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Micrograd Alternatives
Similar projects and alternatives to micrograd
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Exercism - Scala Exercises
Crowd-sourced code mentorship. Practice having thoughtful conversations about code.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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tinygrad
Discontinued You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad] (by geohot)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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ml-coursera-python-assignments
Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.
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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.
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yolov7
Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
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micrograd reviews and mentions
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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.
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A note from our sponsor - SaaSHub
www.saashub.com | 11 May 2024
Stats
karpathy/micrograd is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of micrograd is Jupyter Notebook.
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