☕️ About
sci-ml is an attempt to provide a high-level and human friendly API for Scientific Machine Learning algorithms such as PINN, LSTM-RNN, RC... but with applications in mind.
In the spirit of scikit-learn, the user will find an extensive documentation of the implemented algorithms as well as some practicals use-cases in science and engineering.
Although some implementations and packages already exist, the Python Scientific Machine Learning Community is sparse... Thus, the long-term goal of the project is to provide a constitutive implementation of such algorithms under the same banner.
🎯 Goals
At first the motivations of this project are purely educatives and practicals... So as a researcher using machine/deep learning on a daily basis, i would like to deep dive into it and implement some algorithms in such way that they will be easily reusable and useful for others.
🚀 Features
- Simple and efficient tools for solving science and engineering problems using Machine Learning
- Practical and expressive API
- Stand on the shoulders of giants -> on top of Pandas, Keras, scikit-learn and seaborn.
⚠️ Warnings
For the moment:
- The development of the project takes place in a private GitHub repository
- The project is at a very early stage -> Nothing is implemented in this published version
- The documentation is missing
The GitHub repository as well as a usable Python package will be available in the upcoming months when the project will be more advanced.
🤝 Community-driven
sci-ml is foremost a community-driven project ! The project will be highly collaborative and everyone is welcome to the project ! 🤗
Don't hesitate to contact me if you want to know more or are interested in ! 😃
Stay tuned ! 🗓️
Metadata
Release files for sci-mls 0.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| sci_mls-0.0.0.tar.gz | 2.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| sci_mls-0.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 4.5 kB
Release files / sci_mls-0.0.0.tar.gz
| Download URL | sci_mls-0.0.0.tar.gz |
|---|---|
| Size | 2.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.5.1 CPython/3.11.4 Darwin/22.6.0
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Release files / sci_mls-0.0.0-py3-none-any.whl
| Download URL | sci_mls-0.0.0-py3-none-any.whl |
|---|---|
| Size | 2.3 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
poetry/1.5.1 CPython/3.11.4 Darwin/22.6.0
|