What is the fastest way to get good at Machine Learning? π
Meticulously studying other's code.
Here are 5 of the best git repositories to significantly accelerate your growth in ML π§΅
#ML #Python #MachineLearning
Meticulously studying other's code.
Here are 5 of the best git repositories to significantly accelerate your growth in ML π§΅
#ML #Python #MachineLearning
Vision Transformers
Phil Wang, a.k.a lucidrains has one of the best transformer implementations out there. The code is easy to read, and beautifully structured.
If attention is all you need, lucidrains code is all you need:
github.com
Phil Wang, a.k.a lucidrains has one of the best transformer implementations out there. The code is easy to read, and beautifully structured.
If attention is all you need, lucidrains code is all you need:
github.com
Your Own Deep Learning Library
The entire fast.ai library was built in Jupyter notebooks. You can learn how to construct a state of the art library yourself by studying these notebooks here:
github.com
The entire fast.ai library was built in Jupyter notebooks. You can learn how to construct a state of the art library yourself by studying these notebooks here:
github.com
Autograd from Scratch
As Deep learning practitioners, we take autograd for granted and just focus on modeling. Andrej Karpathy's micrograd repository is a lesson in brevity of code and clarity of function.
Learn how autograd works under the hood here:
github.com
As Deep learning practitioners, we take autograd for granted and just focus on modeling. Andrej Karpathy's micrograd repository is a lesson in brevity of code and clarity of function.
Learn how autograd works under the hood here:
github.com
Machine learning with Scikit-learn
It's easy to forget the classic machine learning methods. Aurelien Geron's Hands on ML book, has a companion repository with examples and exercises for a lot of these:
github.com
It's easy to forget the classic machine learning methods. Aurelien Geron's Hands on ML book, has a companion repository with examples and exercises for a lot of these:
github.com
Traditional ML from Scratch
If Scikit learn whet your appetite for traditional machine learning, check out this gold mine which has from scratch implementations for a lot of these algorithms:
github.com
If Scikit learn whet your appetite for traditional machine learning, check out this gold mine which has from scratch implementations for a lot of these algorithms:
github.com
That's a wrap!
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If you enjoyed this thread:
1. Follow me @DSaience for more on Machine learning, computer vision and being a productive practitioner
2. RT the tweet below to share this thread with your audience
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