kymatio
einops
| kymatio | einops | |
|---|---|---|
| 1 | 22 | |
| 785 | 8,802 | |
| 1.1% | 1.3% | |
| 4.1 | 7.6 | |
| about 2 months ago | about 1 month ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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kymatio
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[P] Fastest wavelet transforms in Python + synchrosqueezing
Also see Kymatio for SOTA on timeseries with limited data, fast and differentiable; nice lecture.
einops
- Einops Rocks
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Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
PyTorch’s ease of use and flexibility, distributed processing, and cloud support make it a good choice for companies looking for open source production-ready solutions. It also has a large ecosystem of tools, such as ParlAI, EinOps, and Accelerate, and a very welcoming community on Slack and PyTorchDiscuss.
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NumPy 2.0.0
https://einops.rocks/#why-use-einops-notation
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Einsum in 40 Lines of Python
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/
- Einops: Flexible and powerful tensor operations for readable and reliable code
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Yorick is an interpreted programming language for scientific simulations
Thanks for the pointer. I can believe that a language that looks so different will find that different patterns and primitives are natural for it.
My experience from writing a lot of array-based code in NumPy/Matlab is that broadcasting absolutely has made it easier to write my code in those ecosystems. Axes of length 1 have often been in the right places already, or have been easy to insert. It's of course possible to create a big mess in any language; it seems likely that the NumPy code you saw could have been neater too.
In machine learning there can be many array dimensions floating around: batch-dims, sequence and/or channel-dims, weight matrices, and so on. It can be necessary to expand two or more dimensions, and/or line up dimensions quite carefully. Einops[1] has emerged from that community as a tool to succinctly express many operations that involve lots of array dimensions. You're likely to bump into more and more people who've used it, and again it seems there's some overlap with what Rank does. (And again, you'll see uses of Einops in the wild that are unnecessarily convoluted.)
[1] https://einops.rocks/ -- It works with all of the existing major array-based frameworks for Python (NumPy/PyTorch/Jax/etc), and the emerging array API standard for Python.
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Torch qeuivalent to image_to_array (keras)
this is definitely what you're looking for: https://github.com/arogozhnikov/einops
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Einops all the things! https://einops.rocks/
- Delimiter-First Code
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[D] Any independent researchers ever get published/into conferences?
It depends on what are their main purposes. I know some figures who have done an amazing job in this field but never because of publications, e.g. https://github.com/lucidrains and https://github.com/rwightman, https://einops.rocks/
What are some alternatives?
ssqueezepy - Synchrosqueezing, wavelet transforms, and time-frequency analysis in Python
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
pywt - PyWavelets - Wavelet Transforms in Python
opt_einsum - ⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
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.
extending-jax - Extending JAX with custom C++ and CUDA code