elyra - Elyra extends JupyterLab Notebooks with an AI centric approach.

•    Python

---

Elyra is a set of AI-centric extensions to JupyterLab Notebooks. The Elyra Getting Started Guide includes more details on these features.

docker machine-learning airflow binder ai anaconda pypi pipelines jupyterlab notebooks hacktoberfest apache-airflow jupyterlab-extensions kubeflow jupyterlab-extension notebook-jupyter kubeflow-pipelines elyra jupyterlab-notebooks

jupyter-dash - Develop Dash apps in the Jupyter Notebook and JupyterLab

•    Python

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This library makes it easy to develop Plotly Dash apps interactively from within Jupyter environments (e.g. classic Notebook, JupyterLab, Visual Studio Code notebooks, nteract, PyCharm notebooks, etc.). See the notebooks/getting_started.ipynb for more information and example usage.

jupyter jupyter-notebook dash plotly-dash

papermill - 📚 Parameterize, execute, and analyze notebooks

•    Python

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Papermill is a tool for parameterizing, executing, and analyzing Jupyter Notebooks. To parameterize your notebook designate a cell with the tag parameters.

jupyter notebooks notebook-generator nteract publishing pipeline

covid19-dashboard - A site that displays up to date COVID-19 stats, powered by fastpages.

•    Jupyter

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This project showcases how you can use fastpages to create a static dashboard that update regularly using Jupyter Notebooks. Using fastpages, data professionals can share dashboards (that are updated with new data automatically) without requiring any expertise in front end development. The content of this site shows statistics and reports regarding Covid-19.

github-pages data-science jupyter analytics nteract data-visualisation matplotlib pymc3 fastai altair papermill github-actions covid-19 covid19 covid-data fastpages

gophernotes - The Go kernel for Jupyter notebooks and nteract.

•    Go

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Acknowledgements - This project utilizes a Go interpreter called gomacro under the hood to evaluate Go code interactively. The gophernotes logo was designed by the brilliant Marcus Olsson and was inspired by Renee French's original Go Gopher design. Important Note - gomacro relies on the plugin package when importing third party libraries. This package works reliably on Mac OS X only with Go 1.10.2+ as long as you never execute the command strip gophernotes. If you can only compile gophernotes with Go <= 1.10.1 on Mac, consider using the Docker install and run gophernotes/Jupyter in Docker.

jupyter jupyter-notebook kernel gophernotes zeromq nteract data-science machine-learning artificial-intelligence numerical-methods

spark-py-notebooks - Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks

•    Jupyter

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This is a collection of IPython notebook/Jupyter notebooks intended to train the reader on different Apache Spark concepts, from basic to advanced, by using the Python language. If Python is not your language, and it is R, you may want to have a look at our R on Apache Spark (SparkR) notebooks instead. Additionally, if your are interested in being introduced to some basic Data Science Engineering, you might find these series of tutorials interesting. There we explain different concepts and applications using Python and R.

spark pyspark data-analysis mllib ipython-notebook notebook ipython data-science machine-learning big-data bigdata

IfSharp - F# for Jupyter Notebooks

•    Jupyter

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This is the F# implementation for Jupyter. View the Feature Notebook for some of the features that are included.You can use Jupyter F# Notebooks for free (with free server-side execution) at Azure Notebooks. If you select "Show me some samples", then there is an "Introduction to F#" which guides you through the language and its use in Jupyter.

f-sharp jupyter jupyter-notebook

graph-notebook - Library extending Jupyter notebooks to integrate with Apache TinkerPop and RDF SPARQL

•    Jupyter

---

The graph notebook provides an easy way to interact with graph databases using Jupyter notebooks. Using this open-source Python package, you can connect to any graph database that supports the Apache TinkerPop, openCypher or the RDF SPARQL graph models. These databases could be running locally on your desktop or in the cloud. Graph databases can be used to explore a variety of use cases including knowledge graphs and identity graphs. We encourage others to contribute configurations they find useful. There is an additional-databases folder where more information can be found.

jupyter graph sparql neptune gremlin jupyter-widgets

CADL - Course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

•    Jupyter

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This repository contains lecture transcripts and homework assignments as Jupyter Notebooks for the first of three Kadenze Academy courses on Creative Applications of Deep Learning w/ Tensorflow. It also contains a python package containing all the code developed during all three courses. The first course makes heavy usage of Jupyter Notebook. This will be necessary for submitting the homeworks and interacting with the guided session notebooks I will provide for each assignment. Follow along this guide and we'll see how to obtain all of the necessary libraries that we'll be using. By the end of this, you'll have installed Jupyter Notebook, NumPy, SciPy, and Matplotlib. While many of these libraries aren't necessary for performing the Deep Learning which we'll get to in later lectures, they are incredibly useful for manipulating data on your computer, preparing data for learning, and exploring results.

jupyter-notebook neural-network tensorflow deep-learning mooc dockerfile machine-learning tutorial workshop

PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks

•    Jupyter

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This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. Run the code using the Jupyter notebooks available in this repository's notebooks directory.

scikit-learn numpy jupyter-notebook matplotlib pandas

nbdime - Tools for diffing and merging of Jupyter notebooks.

•    Python

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nbdime provides tools for diffing and merging of Jupyter Notebooks. See the installation docs for more installation details and development installation instructions.

jupyterlab-extension jupyter jupyter-notebook diff diffing merge git hg mercurial mergetool merge-driver vcs version-control

bokeh-notebooks - Interactive Web Plotting with Bokeh in IPython notebook

•    Jupyter

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Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients. These Jupyter notebooks provide useful Bokeh examples and a tutorial to get started. You can visualize the rendered Jupyter notebooks on NBViewer or download the repository and execute jupyter notebook from your terminal.

best-of-jupyter - 🏆 A ranked list of awesome Jupyter Notebook, Hub and Lab projects (extensions, kernels, tools)

•    

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🏆  A ranked list of awesome Jupyter projects. Updated weekly. 🧙‍♂️  Discover other best-of lists or create your own. 📫  Subscribe to our newsletter for updates and trending projects.

machine-learning awesome deep-learning jupyter notebook jupyter-notebook jupyter-kernels jupyterhub collections jupyterlab jupyter-widget jupyter-notebook-extension jupyterhub-authenticator jupyterlab-extensions jupyter-extension jupyterlab-extension jupyterhub-spawner best-of best-of-list

musicinformationretrieval.com - Instructional notebooks on music information retrieval.

•    Jupyter

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stanford-mir is now musicinformationretrieval.com. This repository contains instructional Jupyter notebooks related to music information retrieval (MIR). Inside these notebooks are Python code snippets that illustrate basic MIR systems.

ipython-notebook music-information-retrieval machine-learning music jupyter-notebook

amazon-sagemaker-examples - Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker

•    Jupyter

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Example Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models.

training aws data-science machine-learning reinforcement-learning deep-learning examples jupyter-notebook inference sagemaker mlops

qiskit-tutorial - A collection of Jupyter notebooks using Qiskit

•    Jupyter

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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.

qiskit quantum-computing tutorial jupyter-notebooks

dashboards - Jupyter Dashboards Layout Extension

•    Jupyter

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The dashboards layout extension is an add-on for Jupyter Notebook. It lets you arrange your notebook outputs (text, plots, widgets, ...) in grid- or report-like layouts. It saves information about your layouts in your notebook document. Other people with the extension can open your notebook and view your layouts. For a sample of what's possible with the dashboard layout extension, have a look at the demo dashboard-notebooks in this repository.

dashboard jupyter jupyter-notebook ipython dashboards

jupyterlab - JupyterLab computational environment.

•    Javascript

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An extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. Currently ready for users. JupyterLab is the next-generation user interface for Project Jupyter. It offers all the familiar building blocks of the classic Jupyter Notebook (notebook, terminal, text editor, file browser, rich outputs, etc.) in a flexible and powerful user interface. Eventually, JupyterLab will replace the classic Jupyter Notebook.

jupyterlab

deepschool.io - Deep Learning tutorials in jupyter notebooks.

•    Jupyter

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See here for installing on windows. 1: Refer to this Dockerfile and this for information on how the docker image was built.

deep-learning keras python3 jupyter-notebook

Python-for-Probability-Statistics-and-Machine-Learning - Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

•    Jupyter

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Jupyter Notebooks for Springer book "Python for Probability, Statistics, and Machine Learning"

jupyter-notebook machine-learning book books probability probability-theory statistics statistics-course statistical-analysis statistical-learning statistical-tests