Displaying 1 to 20 from 31 results

lets-plot - An open-source plotting library for statistical data.

  •    Kotlin

Lets-Plot is an open-source plotting library for statistical data. It is implemented using the Kotlin programming language. The design of Lets-Plot library is heavily influenced by Leland Wilkinson work The Grammar of Graphics describing the deep features that underlie all statistical graphics.

qiskit-tutorial - A collection of Jupyter notebooks using Qiskit

  •    Jupyter

In this repository, we've put together a collection of Jupyter notebooks aimed at teaching people who want to use the QISKit for writing quantum computing programs and executing them on one of several backends (online quantum processors, online simulators, and local simulators). The online quantum processors connects to the IBM Q devices. Please refer to this installation for installing and setting up QISKit and tutorials on your own machine.

Play-with-Machine-Learning-Algorithms - Code of my MOOC Course <Play with Machine Learning Algorithms>

  •    Jupyter

Code of my MOOC Course <Play with Machine Learning Algorithms>. Updated contents and practices are also included. 我在慕课网上的课程《Python3 入门机器学习》示例代码。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。

FEM_resources - Finite Element resources useful for studying or researching

  •    Jupyter

This is a repo for Finite Element resources. I have used this resources for learning (myself) or prototyping some FEM features before implement them in a bigger FEM software. Right now it counts with some wxMaxima worksheets, where the CAS (Computer Algebra System) Maxima is used to compute analytically the elements.

mli-resources - Machine Learning Interpretability Resources

  •    Jupyter

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques is not practical for those who must explain their models to regulators or customers. For many decades, the models created by machine learning algorithms were generally taken to be black-boxes. However, a recent flurry of research has introduced credible techniques for interpreting complex, machine-learned models. Materials presented here illustrate applications or adaptations of these techniques for practicing data scientists. Want to contribute your own examples? Just make a pull request.

readingbricks - A structured collection of tagged notes about machine learning theory and practice endowed with search infrastructure that allows users to read necessary info only

  •    Jupyter

It is a structured collection of tagged notes about machine learning theory and practice (now, in Russian only). Each note is independent of the others, but some of them require familiarity with core concepts and definitions. The last command launches a local server. After it is ready, open your web browser and go to

binder - Binder metapackage for usage, docs, and chat


This repository contains the documentation and usage instructions for the mybinder.org service. For deployment of the website mybinder.org, please visit mybinder.org-deploy.

notebooks - interactive notebooks from Planet Engineering

  •    Jupyter

In this repository, you'll find a collection of Jupyter notebooks from the software developers, data scientists, and developer advocates at Planet. These interactive, open-source (APLv2) guides are designed to help you explore Planet data, work with our APIs and tools, and learn how to extract information from our massive archive of high-cadence satellite imagery. We hope these guides will inspire you to ask interesting questions of Planet data. Need help? Find a bug? Please file an issue and we'll get back to you. Soon we hope to add notebooks from the researchers, technologists, geographers, and entrepreneurs who are already using Planet data to ask interesting and innovative questions about our changing Earth. If you're working with our imagery and have a notebook (or just an idea for a notebook) that you'd like to share, please file an issue and let us know.

aws-iot-analytics-notebook-containers - An extension for Jupyter notebooks that allows running notebooks inside a Docker container and converting them to runnable Docker images

  •    Python

An extension for Jupyter notebooks that allows running notebooks insider a Docker container and converting them to executable Docker images. The extension relies on certain configurations specific to Jupyter notebooks offered by Amazon SageMaker service. In particular, it assumes that Docker is installed, relies on Environment Kernels plugin and specific kernel name prefix to create matching containerized kernels. It also expects certain directories to be present (in order to mount them to the container) and puts the notebook output to a specific S3 location.

jupyterWith - declarative and reproducible Jupyter environments - powered by Nix

  •    Jupyter

This repository provides a Nix-based framework for the definition of declarative and reproducible Jupyter environments. These environments include JupyterLab - configurable with extensions - the classic notebook, and configurable Jupyter kernels. In practice, a Jupyter environment is defined in a single shell.nix file which can be distributed together with a notebook as a self-contained reproducible package.

AMLSamples - Repo hosting examples using Azure Machine learning SDK

  •    Jupyter

This repository provides examples as jupyter notebooks on how to use Azure Machine learning service python SDK. This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

notebooks - :notebook: A growing collection of Jupyter Notebooks written in Python, OCaml and Julia for science examples, algorithms, visualizations etc

  •    Jupyter

This repository hosts some Jupyter Notebooks, covering various subjects. Go to nbviewer to read them. At the beginning, this repository was only here to host some small experiments, for me to learn how to use the wonderful Jupyter tools correctly (baby notebooks 🍼)...

quantum_notebooks - Jupyter Notebooks that demonstrate SymPy's symbolic quantum mechanics package.

  •    Jupyter

This repository contains a set of Jupyter Notebooks that demonstrate the capabilities of sympy.physics.quantum. You can browse static version of these notebooks here on GitHub, or click the binder badge below to launch a live Jupyter Notebook server with the notebooks in this repo.

xyz-spaces-python - Manage your XYZ Hub or HERE Data Hub spaces from Python.

  •    Python

Manage your XYZ Hub or HERE Data Hub spaces from Python. FEATURED IN: Online Python Machine Learning Conference & GeoPython 2020, Sept 21, 2020, see conference schedule.

jupyter-innotater - Inline data annotator for Jupyter notebooks

  •    Python

Annotate data including image bounding boxes inline within your Jupyter notebook in Python. Innotater's flexible API allows easy selection of interactive controls to suit your datasets exactly. In a data science or machine learning project, you may prepare and study images or other data within a Jupyter notebook then need to annotate the data to augment the training or fix errors in your source data.

nb2xls - Convert Jupyter notebook to Excel spreadsheet

  •    Jupyter

Convert Jupyter notebooks to Excel Spreadsheets (xlsx), through a new 'Download As' option or via nbconvert on the command line. Respects tables such as Pandas DataFrames. Also exports image data such as matplotlib output.

portfolio - Site built from fastpages: https://fastpages.fast.ai/. Deployed here 👉

  •    Jupyter

Note: you may want to remove example blog posts from the _posts, _notebooks or _word folders (but leave them empty, don't delete these folders) if you don't want these blog posts to appear on your site. Please use the nbdev & blogging channel in the fastai forums for any questions or feature requests.

We have large collection of open source products. Follow the tags from Tag Cloud >>

Open source products are scattered around the web. Please provide information about the open source projects you own / you use. Add Projects.