Machine-Learning-Specialization-Coursera
micrograd
Machine-Learning-Specialization-Coursera | micrograd | |
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6 | 22 | |
2,728 | 8,330 | |
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4.1 | 0.0 | |
6 days ago | 11 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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Machine-Learning-Specialization-Coursera
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Linear Algebra for Programmers
I cannot recommend Andrew Ng's courses on Machine Learning enough. Something like this seems like it would cover everything you're looking for.
https://www.coursera.org/learn/machine-learning
I cannot speak to the author of the content of this github repo, but it appears they have completed the course and included all of the solutions here. It might let you jump right to what you're looking for.
https://github.com/greyhatguy007/Machine-Learning-Specializa...
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Stupid question but help with Andrew Ng course
github link : GitHub - greyhatguy007/Machine-Learning-Specialization-Coursera: Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
- Machine Learning Specialization
- Alternatives to Andrew Ng's Machine Learning course on Coursera
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Is there no way to do the labs exercises on Coursera unless I pay for it?
Found this and it may have some of the labs https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera
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I'm a highly motivated undergrad from a 3rd world country who is unable to afford the paid version of new Andrew NG course. What can I do about the labs?
https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera This guy has added all the quizzes and labs of this course.
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?
Reinforcement-Learning-2nd-Edition-by-Sutton-Exercise-Solutions - Solutions of Reinforcement Learning, An Introduction
deepnet - Educational deep learning library in plain Numpy.
cs229-2018-autumn - All notes and materials for the CS229: Machine Learning course by Stanford University
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️ [Moved to: https://github.com/tinygrad/tinygrad]
Spotify_Song_Recommender - This project leverages spotify's api and provided user playlists to create and tune a neural network model that generates song recommendations based off of song data in provided playlists.
deeplearning-notes - Notes for Deep Learning Specialization Courses led by Andrew Ng.
coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
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.
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
NNfSiX - Neural Networks from Scratch in various programming languages
handson-ml - ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
yolov7 - Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors