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Top 23 Python Numpy Projects
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30-Days-Of-Python
30 days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than100 days, follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
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data-science-ipython-notebooks
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
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datasets
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scientific-visualization-book
An open access book on scientific visualization using python and matplotlib
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einops
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
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mars
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Hi everyone! I’m devloker, and today I’m excited to share a project I’ve been working on: a digit recognition system implemented using pure math functions in Python. This project aims to help beginners grasp the mathematics behind AI and digit recognition without relying on high-level libraries like TensorFlow or PyTorch. You can find the complete code on my GitHub repository.
4. Asabeneh/30-Days-Of-Python - This repository presents a 30-day challenge for beginners to learn Python from the ground up. The course covers everything from the basics to more advanced topics like statistics, data analysis, and web development. https://github.com/Asabeneh/30-Days-Of-Python
NumPy: This library is fundamental for handling arrays and matrices, such as for operations that involve image data. NumPy is used to manipulate image data and perform calculations for image transformations and mask operations.
Project mention: 🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇 | dev.to | 2023-10-19
See also https://github.com/unifyai/ivy which I have not tried but seems along the lines of what you are describing, working with all the major frameworks
Project mention: Book to practice along Mckinney's "Python for data analysis" | /r/learnpython | 2023-06-16Check out this numpy 100 exercises repo.
Project mention: Scientific Visualization: Python and Matplotlib | news.ycombinator.com | 2024-02-13
Project mention: Nvidia Warp: A Python framework for high performance GPU simulation and graphics | news.ycombinator.com | 2024-06-14
Not sure if the wrapper you’re talking about is your own custom code, but I really like using einops lately. It’s got similar axis naming capabilities and it dispatches to both numpy and pytorch
http://einops.rocks/
Project mention: Maxtext: A simple, performant and scalable Jax LLM | news.ycombinator.com | 2024-04-23Is t5x an encoder/decoder architecture?
Some more general options.
The Flax ecosystem
https://github.com/google/flax?tab=readme-ov-file
or dm-haiku
https://github.com/google-deepmind/dm-haiku
were some of the best developed communities in the Jax AI field
Perhaps the “trax” repo? https://github.com/google/trax
Some HF examples https://github.com/huggingface/transformers/tree/main/exampl...
Sadly it seems much of the work is proprietary these days, but one example could be Grok-1, if you customize the details. https://github.com/xai-org/grok-1/blob/main/run.py
Project mention: Mojo: Ownership and lifetime checks deep dive with Chris Lattner [video] | news.ycombinator.com | 2024-05-13I think I would agree with you. In my opinion, that already exists and is decently mature. CuPy [0] for Python and CUDA.jl [1] for Julia are both excellent ways to interface with GPU that don't require you to get into the nitty gritty of CUDA. Both do their best to keep you at the Array-level abstraction until you actually need to start writing kernels yourself and even then, it's pretty simple. They took a complete GPU novice like me and let me to write pretty performant kernels without having to ever touch raw CUDA.
[0] https://cupy.dev/
Project mention: ChaiNNer – Node/Graph based image processing and AI upscaling GUI | news.ycombinator.com | 2023-07-19There is already an AI framework named Chainer: https://github.com/chainer/chainer
(https://github.com/ijl/orjson). In this implementation, the same JSON
I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.
Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.
https://orangedatamining.com/
https://orange3.readthedocs.io/projects/orange-visual-progra...
PyQtGraph - Interactive and realtime 2D/3D/Image plotting and science/engineering widgets.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python Numpy discussion
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Tinygrad 0.9.0
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A note from our sponsor - InfluxDB
www.influxdata.com | 15 Jun 2024
Index
What are some of the best open-source Numpy projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Pytorch | 79,328 |
| 2 | 30-Days-Of-Python | 32,933 |
| 3 | NumPy | 26,774 |
| 4 | data-science-ipython-notebooks | 26,600 |
| 5 | datasets | 18,647 |
| 6 | ivy | 14,028 |
| 7 | Dask | 12,157 |
| 8 | numpy-100 | 11,690 |
| 9 | scientific-visualization-book | 10,131 |
| 10 | Numba | 9,556 |
| 11 | mlcourse.ai | 9,502 |
| 12 | einops | 8,075 |
| 13 | trax | 7,980 |
| 14 | cupy | 7,900 |
| 15 | tensorboardX | 7,817 |
| 16 | chainer | 5,871 |
| 17 | orjson | 5,706 |
| 18 | orange | 4,665 |
| 19 | datasets | 4,217 |
| 20 | PyQtGraph | 3,730 |
| 21 | xarray | 3,455 |
| 22 | mars | 2,680 |
| 23 | napari | 2,100 |
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